Patent Publication Number: US-8527235-B2

Title: Object position estimation system, object position estimation device, object position estimation method and object position estimation program

Description:
TECHNICAL FIELD 
     The present invention relates to an object position estimation system that identifies an object and estimates its position based upon pieces of information of a plurality of different kinds of sensors (observation devices), and also concerns an object position estimation device, an object position estimation method and an object position estimation program. 
     BACKGROUND ART 
     Radio tags or cameras are often used as sensors capable of detecting positions of objects. 
     Although the radio tags basically never fail to identify the ID of an object (because the ID identification of an object is carried out based upon ID information transmitted by a transmission tag attached to the object to be identified), their position measuring precision is not good in comparison with that of the camera. Moreover, for example, another issue arises in which, since radio waves are absorbed by moisture, the detection of the tag becomes instable. For this reason, under a general environment in which, different from a factory or the like, the environment is not properly prepared, the position measuring precision of the radio tag is not expected so much. In particular, for example, when a person or the like containing much moisture is carrying a radio tag, the positional error might become 1 m or more, or the tag itself might not be detected. 
     Moreover, in the case of the camera, its position measuring precision is better than that of the radio tag, when the detection of an object is appropriately carried out; however, the object ID identification precision cannot be set to 100% (because the object ID is identified based upon image-based features obtained by the camera (shape, color, or the like)). In particular, the identification rates of objects having similar image-based features become lower. For example, it is very difficult to identify objects having similar colors or shapes, such as a tomato, an apple, and the like, with high precision. 
     In this manner, since degrees of detection performances are different depending on the respective kinds of sensors, it is difficult to identify an object and also to determine its position by using only one kind of a sensor. Therefore, a technique has been proposed in which a plurality of kinds of sensors are combined with one another, and by integrating pieces of observation information of the plurality of sensors of different kinds, the position of an object is estimated while compensating for their insufficiencies in observation precision of the sensors of the respective kinds (Non-Patent Document 1). 
     PRIOR ART DOCUMENTS 
     Non-Patent Documents 
     Non-Patent Document 1: Hirofumi Kanazaki, Takehisa Yairi, Kazuo Machida, Kenji Kondo, and Yoshihiko Matsukawa, “Variational Approximation Data Association Filter”, 15 th  European Signal Processing Conference (EUSIPC02007), Publication Date: Sep. 3, 2007, p 1872-p 1876. 
     SUMMARY OF INVENTION 
     Issues to be Solved by the Invention 
     However, in order to use the above-mentioned technique for estimating the object position, it is the premise that all the observation devices can identify the ID of an object. However, in the case when tracking processes of the position of an object or a person in an actual environment are taken into consideration, there are some cases in which the premise that the ID identification is available in all the observation devices is not satisfied. 
     For example, in the case when an identifying process of a person is carried out by using a camera as the observation device, it is easily assumed that there is a state in which a person is wearing different closes every day or a state in which a plurality of persons are wearing the same uniforms. In such a case, it is difficult to identify persons based upon the amount of feature of the colors of the clothes obtained by the camera in an actual state. Alternatively, even in the case when the amount of feature of a face image is used for identifying persons, since the person does not necessarily walk with his or her face always facing in the camera direction, it is not possible to ensure the stable identifying process of the persons. A system may be proposed in which a plurality of cameras are arranged all over the place; however, such a system structure and a system installation cause extremely high costs, making it difficult to put it into practical use from the view point of the cost-benefit ratio. 
     On the other hand, with respect to the ID identification of articles that hardly cause changes in features of the colors or the shapes, although it is stably carried out by using the camera, conditions that satisfy such a premise are extremely limited, to cause a bottle neck in practical use. 
     As described above, the technique disclosed in the prior art (non-patent document 1) has an issue in that, when an observation device that fails to carry out an ID identification is included, the position estimation of an object cannot be carried out. At the same time, another issue is that a state in which the prerequisite of the prior art is satisfied is extremely limited with the result that its application range is very narrow. 
     In view of these circumstances, the objective of the present invention is to provide an object position estimation system which can estimate a position of an object even when an observation device incapable of object ID identification, typically represented by a camera, is included therein, as well as an object position estimation device, an object estimation method, and an object position estimation program. More specifically, the present invention calculates a numeric value corresponding to the ID likelihood of an object observed by an observation device incapable of ID identification, based upon observation information given those observation devices other than the observation device incapable of ID identification, so that the conventional issues can be solved. 
     Means for Solving the Issues 
     In order to achieve the above-mentioned object, the present invention has the following structures: 
     According to a first aspect of the present invention, there is provided an object position estimation system, which estimates a position of an object, characterized by comprising: 
     a first observation unit that observes the object at respectively different points of time so that pieces of first observation information including positions and IDs of the object is acquired; 
     a first object position likelihood determination unit that, based upon the pieces of first observation information respectively observed in the first observation unit, determines first object position likelihoods corresponding to estimated positions of the object at each of the respective points of time; 
     a first object ID likelihood determination unit that, based upon the first object position likelihood determined by the first object position likelihood determination unit, determines first object ID likelihoods of the object at the respective points of time; 
     a second observation unit that observes the object at respectively different points of time to respectively acquire pieces of second observation information including positions and amounts of feature of the object and attaches second observation IDs to the pieces of second observation information; 
     a second object position likelihood determination unit that, based upon the pieces of the second observation information respectively observed in the second observation unit, determines second object position likelihoods corresponding to estimated positions of the object at the respective points of time; 
     an object tracking state determination unit that detects two pieces of the second observation information having a same amount of feature of the object, obtained at different points of time, and determines, by making the second observation IDs of the two pieces of the second observation information thus detected mutually associated with each other, tracking state information of the object; 
     a second object ID likelihood determination unit that, based upon the tracking state information of the object and the estimated positions of the object, determines second object ID likelihoods of the second observation information; 
     an association unit that, based upon the first object ID likelihoods and the first object position likelihoods of the object, calculates a first object association value, and based upon the second object ID likelihoods and the second object position likelihoods, also calculates a second object association value; and 
     an object position estimation unit that, based upon the first object ID likelihoods, the first object position likelihoods, and the first object association value of the object and/or the second object ID likelihoods, the second object position likelihoods, and the second object association value of the object, estimates the position of the object. 
     According to a sixth aspect of the present invention, there is provided an object position estimation method, which estimates a position of an object, characterized by comprising: 
     observing the object at respectively different points of time so that pieces of first observation information including positions and IDs of the object is acquired by a first observation unit; 
     based upon the pieces of first observation information respectively observed in the first observation unit, determining first object position likelihoods corresponding to estimated positions of the object at each of the respective points of time by using a first object position likelihood determination unit; 
     based upon the first object position likelihoods determined by the first object position likelihood determination unit, determining first object ID likelihoods of the object at the respective points of time by using a first object ID likelihood determination unit; 
     observing the object at respectively different points of time to respectively acquire pieces of second observation information including the positions and amounts of feature of the object by using a second observation unit and attaching second observation IDs to the pieces of second observation information by using the second observation unit; 
     based upon the pieces of the second observation information respectively observed in the second observation unit, determining second object position likelihoods corresponding to estimated positions of the object at the respective points of time by using a second object position likelihood determination unit; 
     detecting two pieces of the second observation information having a same amount of feature of the object, obtained at different points of time, and determining, by making the second observation IDs of the two pieces of the second observation information thus detected mutually associated with each other, tracking state information of the object by an object tracking state determination unit; 
     based upon the tracking state information of the object and the estimated positions of the object, determining second object ID likelihoods of the second observation information by using a second object ID likelihood determination unit; 
     based upon the first object ID likelihoods and the first object position likelihoods of the object, calculating a first object association value by using an association unit; 
     based upon the second object ID likelihoods and the second object position likelihoods of the object, also calculating a second object association value by using the association unit; and 
     based upon the first object ID likelihoods, the first object position likelihoods, and the first object association value of the object and/or the second object ID likelihoods, the second object position likelihoods, and the second object association value of the object, estimating the position of the object by using an object position estimation unit. 
     According to a seventh aspect of the present invention, there is provided an object position estimation program, which allows a computer to carry out functions of: 
     observing the object at respectively different points of time so that pieces of first observation information including positions and IDs of the object is acquired by a first observation unit; 
     based upon the pieces of first observation information respectively observed in the first observation unit, determining first object position likelihoods corresponding to estimated positions of the object at the respective points of time by using a first object position likelihood determination unit; 
     based upon the first object position likelihoods determined by the first object position likelihood determination unit, determining first object ID likelihoods of the object at the respective points of time by using a first object ID likelihood determination unit; 
     observing the object at respectively different points of time to respectively acquire pieces of second observation information including the positions and amounts of feature of the object by using a second observation unit and attaching second observation IDs to the pieces of second observation information by using the second observation unit; 
     based upon the pieces of the second observation information respectively observed in the second observation unit, determining second object position likelihoods corresponding to estimated positions of the object at the respective points of time by using a second object position likelihood determination unit; 
     detecting two pieces of the second observation information having a same amount of feature of the object, obtained at different points of time, and determining, by making the second observation IDs of the two pieces of the second observation information thus detected mutually associated with each other, tracking state information of the object by an object tracking state determination unit; 
     based upon the tracking state information of the object and the estimated positions of the object, determining second object ID likelihoods of the second observation information by using a second object ID likelihood determination unit; 
     based upon the first object ID likelihoods and the first object position likelihoods of the object, calculating a first object association value by using an association unit; 
     based upon the second object ID likelihoods and the second object position likelihoods, calculating a second object association value by using the association unit; and 
     based upon the first object ID likelihoods, the first object position likelihoods, and the first object association value of the object and/or the second object ID likelihoods, the second object position likelihoods, and the second object association value of the object, estimating the position of the object by using an object position estimation unit. 
     According to an eighth aspect of the present invention, there is provided an object position estimation device comprising: 
     a first object position likelihood determination unit that, based upon pieces of first observation information from a first observation unit that observes an object at respectively different points of time to acquire the pieces of first observation information including positions and IDs of the object, determines first object position likelihoods corresponding to estimated positions of the object at the respective points of time; 
     a first object ID likelihood determination unit that, based upon the first object position likelihood determined by the first object position likelihood determination unit, determines first object ID likelihoods of the object at the respective points of time; 
     a second object position likelihood determination unit that, based upon pieces of second observation information from a second observation unit that observes an object at respectively different points of time to acquire the pieces of second observation information including the positions and amounts of feature of the object and to attach second observation IDs to the pieces second observation information, determines second object position likelihoods corresponding to estimated positions of the object at the respective points of time; 
     an object tracking state determination unit that detects two pieces of the second observation information having a same amount of feature of the object, obtained at different points of time, and determines, by making the second observation IDs of the two pieces of the second observation information thus detected mutually associated with each other, tracking state information of the object; 
     a second object ID likelihood determination unit that, based upon the tracking state information of the object and the estimated positions of the object, determines second object ID likelihoods of the second observation information; 
     an association unit that, based upon the first object ID likelihoods and the first object position likelihoods of the object, calculates a first object association value, and based upon the second object ID likelihoods and the second object position likelihoods, also calculates a second object association value; and 
     an object position estimation unit that, based upon at least either one of the groups including (1) the first object ID likelihoods, the first object position likelihoods, and the first object association value of the object, and (2) the second object ID likelihoods, the second object position likelihoods, and the second object association value of the object, estimates the position of the object. 
     In accordance with the present structure, the amount corresponding to the ID likelihood of an object observed by the second observation device can be calculated based upon observation information of the first observation device capable of ID identification, so that, even when an ID identifying function of an object is not provided in the second observation device, it becomes possible to estimate the position of an object. 
     Effects of the Invention 
     In accordance with the object position estimation system, object position estimation device, object position estimation method, and object position estimation program of the present invention, ID likelihood of an object can be determined based upon information from those observation devices other than the observation device incapable of ID identification. For this reason, even when an observation device is not provided with an ID identifying function of an object, it is possible to estimate the position of the object. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       These and other aspects and features of the present invention will become clear from the following description taken in conjunction with the preferred embodiments thereof with reference to the accompanying drawings, in which: 
         FIG. 1A  is a block diagram that shows a structure of an object position estimation system in accordance with a first embodiment of the present invention; 
         FIG. 1B  is a block diagram that shows a structure of an object position estimation device in accordance with a modified example of the first embodiment of the present invention; 
         FIG. 2  is a graph that shows an operation example of Kalman filter to be used in the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 3  is an explanatory view that shows a room serving as a life space that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 4  is a drawing that shows an example of a person ID conversion table in the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 5  is a drawing that shows an example of an output history of a first observation device of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 6  is a drawing that shows an example of an output history of a second observation device of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 7  is a drawing that shows an example of an output history of an object position estimation means of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 8  is a drawing that shows an example of an output history of a second object ID likelihood determination means of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 9A  is a drawing that shows an example of an actual position of a person at the time of 2008/09/02 — 12:00:00 in a room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 9B  is a drawing that shows an example of an actual position of the person at the time of 2008/09/02 — 12:00:01 in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 9C  is a drawing that shows an example of an actual position of the person at the time of 2008/09/02 — 12:00:02 in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 10A  is a drawing that shows an example of a detected position (observation position) of a person at the time of 2008/09/02 — 12:00:00 in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 10B  is a drawing that shows an example of a detected position (observation position) of the person at the time of 2008/09/02 — 12:00:01 in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 10C  is a drawing that shows an example of a detected position (observation position) of the person at the time of 2008/09/02 — 12:00:02 in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 11A  is a drawing that shows an example of an initial position (position prior to an application of an observation value) in the room that is an observation object, upon activation of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 11B  is a drawing that shows an example of an estimated position (position after the application of the observation value) of a person at the time of 2008/09/02 — 12:00:00 in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 11C  is a drawing (the same view as  FIG. 11B ) that shows an example of an initial position (position prior to an application of an observation value) at the time of 2008/09/02 — 12:00:01 in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 11D  is a drawing that shows an example of an estimated position (position after the application of the observation value) of the person at the time of 2008/09/02 — 12:00:01 in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 11E  is a drawing (the same view as  FIG. 11D ) that shows an example of an initial position (position prior to an application of an observation value) at the time of 2008/09/02 — 12:00:02 in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 11F  is a drawing that shows an example of an estimated position (position after the application of the observation value) of the person at the time of 2008/09/02 — 12:00:02 in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 12A  is a drawing that shows a distance between a person and a detection position at time T in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 12B  is a drawing that shows a distance between the person and the detection position at time T+1 in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 13  is a drawing that shows a person detection position at time T+2 in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 14  is a flow chart that shows processes of the first observation device in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 15  is a flow chart that shows processes of the second observation device in the room that is an observation object of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 16  is a flow chart that shows processes of the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 17  is a drawing that explains an observation state of a camera in the case when two persons having the same amount of color characteristic respectively pass each other, in the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 18  is a drawing that shows a setting of reference time in the object position estimation system in accordance with the first embodiment of the present invention; 
         FIG. 19A  is a drawing that shows one example of an environment map (environment map information) provided in the object position estimation system in accordance with the first embodiment of the present invention; and 
         FIG. 19B  is a drawing that shows one example of an environment map provided in the camera in the object position estimation system in accordance with the first embodiment of the present invention. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Referring to Figs., the following description will discuss embodiments of the present invention. 
     Prior to the detailed explanation of the embodiments of the present invention by reference to Figs., various modes of the present invention will be explained. 
     According to a first aspect of the present invention, there is provided an object position estimation system, which estimates a position of an object, characterized by comprising: 
     a first observation unit that observes the object at respectively different points of time so that pieces of first observation information including positions and IDs of the object is acquired; 
     a first object position likelihood determination unit that, based upon the pieces of first observation information respectively observed in the first observation unit, determines first object position likelihoods corresponding to estimated positions of the object at each of the respective points of time; 
     a first object ID likelihood determination unit that, based upon the first object position likelihood determined by the first object position likelihood determination unit, determines first object ID likelihoods of the object at the respective points of time; 
     a second observation unit that observes the object at respectively different points of time to respectively acquire pieces of second observation information including positions and amounts of feature of the object and attaches second observation IDs to the pieces of second observation information; 
     a second object position likelihood determination unit that, based upon the pieces of the second observation information respectively observed in the second observation unit, determines second object position likelihoods corresponding to estimated positions of the object at the respective points of time; 
     an object tracking state determination unit that detects two pieces of the second observation information having a same amount of feature of the object, obtained at different points of time, and determines, by making the second observation IDs of the two pieces of the second observation information thus detected mutually associated with each other, tracking state information of the object; 
     a second object ID likelihood determination unit that, based upon the tracking state information of the object and the estimated positions of the object, determines second object ID likelihoods of the second observation information; 
     an association unit that, based upon the first object ID likelihoods and the first object position likelihoods of the object, calculates a first object association value, and based upon the second object ID likelihoods and the second object position likelihoods, also calculates a second object association value; and 
     an object position estimation unit that, based upon the first object ID likelihoods, the first object position likelihoods, and the first object association value of the object and/or the second object ID likelihoods, the second object position likelihoods, and the second object association value of the object, estimates the position of the object. 
     According to a second aspect of the present invention, there is provided the object position estimation system according to the first aspect, characterized in that the object tracking state determination unit outputs a tracking success likelihood that indicates a probability of successful tracking of the object and a tracking failure likelihood that indicates a probability of failure in tracking the object, and 
     the second object ID likelihood determination unit provides a sum of a value obtained by multiplying the association value calculated upon a previous detection of the object by the tracking success likelihood and a value obtained by dividing the tracking failure likelihood by a number of all the objects serving as detection subjects, as the second ID likelihood of the object. 
     According to a third aspect of the present invention, there is provided The object position estimation system according to the first or second aspect, characterized in that in a case when by detecting two pieces of the second observation information having a same amount of feature, obtained at different points of time, the object tracking state determination unit determines that the object detected by the second observation device is being tracked, the association unit finds the ID of the object detected by the second observation device, based upon the ID of the object and the position of the object estimated by the object position estimation unit. 
     According to a fourth aspect of the present invention, there is provided the object position estimation system according to any one of the first to third aspects, characterized by further comprising: 
     an environment map in which entrance information including a position of an entrance through which the persons that are present in the environment get in or get out, or blind spot information of the first observation device, or blind spot information of the second observation device is recorded. 
     According to a fifth aspect of the present invention, there is provided the object position estimation system according to any one of the first to fourth aspects, characterized in that the object tracking state determination unit determines probability of detecting a plurality of overlapped objects as a single object. 
     According to a sixth aspect of the present invention, there is provided an object position estimation method, which estimates a position of an object, characterized by comprising: 
     observing the object at respectively different points of time so that pieces of first observation information including positions and IDs of the object is acquired by a first observation unit; 
     based upon the pieces of first observation information respectively observed in the first observation unit, determining first object position likelihoods corresponding to estimated positions of the object at each of the respective points of time by using a first object position likelihood determination unit; 
     based upon the first object position likelihoods determined by the first object position likelihood determination unit, determining first object ID likelihoods of the object at the respective points of time by using a first object ID likelihood determination unit; 
     observing the object at respectively different points of time to respectively acquire pieces of second observation information including the positions and amounts of feature of the object by using a second observation unit and attaching second observation IDs to the pieces of second observation information by using the second observation unit; 
     based upon the pieces of the second observation information respectively observed in the second observation unit, determining second object position likelihoods corresponding to estimated positions of the object at the respective points of time by using a second object position likelihood determination unit; 
     detecting two pieces of the second observation information having a same amount of feature of the object, obtained at different points of time, and determining, by making the second observation IDs of the two pieces of the second observation information thus detected mutually associated with each other, tracking state information of the object by an object tracking state determination unit; 
     based upon the tracking state information of the object and the estimated positions of the object, determining second object ID likelihoods of the second observation information by using a second object ID likelihood determination unit; 
     based upon the first object ID likelihoods and the first object position likelihoods of the object, calculating a first object association value by using an association unit; 
     based upon the second object ID likelihoods and the second object position likelihoods of the object, also calculating a second object association value by using the association unit; and 
     based upon the first object ID likelihoods, the first object position likelihoods, and the first object association value of the object and/or the second object ID likelihoods, the second object position likelihoods, and the second object association value of the object, estimating the position of the object by using an object position estimation unit. 
     According to a seventh aspect of the present invention, there is provided an object position estimation program, which allows a computer to carry out functions of: 
     observing the object at respectively different points of time so that pieces of first observation information including positions and IDs of the object is acquired by a first observation unit; 
     based upon the pieces of first observation information respectively observed in the first observation unit, determining first object position likelihoods corresponding to estimated positions of the object at the respective points of time by using a first object position likelihood determination unit; 
     based upon the first object position likelihoods determined by the first object position likelihood determination unit, determining first object ID likelihoods of the object at the respective points of time by using a first object ID likelihood determination unit; 
     observing the object at respectively different points of time to respectively acquire pieces of second observation information including the positions and amounts of feature of the object by using a second observation unit and attaching second observation IDs to the pieces of second observation information by using the second observation unit; 
     based upon the pieces of the second observation information respectively observed in the second observation unit, determining second object position likelihoods corresponding to estimated positions of the object at the respective points of time by using a second object position likelihood determination unit; 
     detecting two pieces of the second observation information having a same amount of feature of the object, obtained at different points of time, and determining, by making the second observation IDs of the two pieces of the second observation information thus detected mutually associated with each other, tracking state information of the object by an object tracking state determination unit; 
     based upon the tracking state information of the object and the estimated positions of the object, determining second object ID likelihoods of the second observation information by using a second object ID likelihood determination unit; 
     based upon the first object ID likelihoods and the first object position likelihoods of the object, calculating a first object association value by using an association unit; 
     based upon the second object ID likelihoods and the second object position likelihoods, calculating a second object association value by using the association unit; and 
     based upon the first object ID likelihoods, the first object position likelihoods, and the first object association value of the object and/or the second object ID likelihoods, the second object position likelihoods, and the second object association value of the object, estimating the position of the object by using an object position estimation unit. 
     According to an eighth aspect of the present invention, there is provided an object position estimation device comprising: 
     a first object position likelihood determination unit that, based upon pieces of first observation information from a first observation unit that observes an object at respectively different points of time to acquire the pieces of first observation information including positions and IDs of the object, determines first object position likelihoods corresponding to estimated positions of the object at the respective points of time; 
     a first object ID likelihood determination unit that, based upon the first object position likelihood determined by the first object position likelihood determination unit, determines first object ID likelihoods of the object at the respective points of time; 
     a second object position likelihood determination unit that, based upon pieces of second observation information from a second observation unit that observes an object at respectively different points of time to acquire the pieces of second observation information including the positions and amounts of feature of the object and to attach second observation IDs to the pieces second observation information, determines second object position likelihoods corresponding to estimated positions of the object at the respective points of time; 
     an object tracking state determination unit that detects two pieces of the second observation information having a same amount of feature of the object, obtained at different points of time, and determines, by making the second observation IDs of the two pieces of the second observation information thus detected mutually associated with each other, tracking state information of the object; 
     a second object ID likelihood determination unit that, based upon the tracking state information of the object and the estimated positions of the object, determines second object ID likelihoods of the second observation information; 
     an association unit that, based upon the first object ID likelihoods and the first object position likelihoods of the object, calculates a first object association value, and based upon the second object ID likelihoods and the second object position likelihoods, also calculates a second object association value; and 
     an object position estimation unit that, based upon at least either one of the groups including (1) the first object ID likelihoods, the first object position likelihoods, and the first object association value of the object, and (2) the second object ID likelihoods, the second object position likelihoods, and the second object association value of the object, estimates the position of the object. 
     Referring to Figs., the following description will discuss an object position estimation system, an object position estimation device, an object position estimation method that is carried out by such an object position estimation device or an object position estimation system, and an object position estimation program. 
     First Embodiment 
     &lt;System Structure&gt; 
       FIG. 1A  is a drawing that shows a structure of an object position estimation system in accordance with a first embodiment of the present invention. 
     The object position estimation system in accordance with the first embodiment of the present invention is configured by a first observation device  101 , a second observation device  102 , a second object ID likelihood determination means  107  (second object ID likelihood determination unit), an object position estimation means  108  (object position estimation unit), and an association means  109  (association unit). 
     The first observation device  101  is provided with a first object position likelihood determination means  103  (first object position likelihood determination unit), a first detection unit  101   a  functioning as a first observation unit, a first object ID likelihood determination means  104  (first object ID likelihood determination unit), and a first inner storage unit  110 . The second observation device  102  is provided with a second detection unit  102   a  functioning as a second observation unit, an image processing unit  102   b , a second object position likelihood determination means  105  (second object position likelihood determination unit), and an object tracking state determination means  106  (object tracking state determination unit), and a second inner storage unit  111 . 
     Additionally, as a modified example of the object position estimation system in accordance with the first embodiment of the present invention, as shown in  FIG. 18 , instead of respectively forming the first observation device  101  and the second observation device  102  by using a plurality of components, those components may be individually prepared, and components except for a first detection unit  101   a  serving as a first observation unit, a first timer  101   t , a second detection unit  102   a  serving as a second observation unit, and a second timer  102   t  may be prepared as an object position estimation device  99 . In other words, in the object position estimation device  99  of  FIG. 1B , the first observation device  101  is formed in the same manner as in the object position estimation system relating to the first embodiment shown in  FIG. 1A , and an image processing unit  102   b  is added thereto so that a second observation device  102  is formed; thus, the same structure as that of the object estimation system relating to the first embodiment of  FIG. 1A  may be obtained. 
     In this case, the object position estimation device  99  is provided with a first object position likelihood determination means  103  (first object position likelihood determination unit), a first object ID likelihood determination means  104  (first object position likelihood determination unit), a second object position likelihood determination means  105  (second object position likelihood determination unit), an object tracking state determination means  106  (object tracking state determination unit), a second object ID likelihood determination means  107  (second object ID likelihood determination unit), an association means  109  (association unit), and an object position estimation means  108  (object position estimation unit), as its main components. As additional components, the object position estimation device  99  may be provided with a first storage unit  110   m  and a second storage unit  111   m . In this structure, information from the first detection unit  101   a  functioning as the first observation unit and the first timer  101   t  may be inputted to the first object position likelihood determination means  103  (first object position likelihood determination unit), and also stored in the first storage unit  110   m . Moreover, information from the first object position likelihood determination means  103  (first object position likelihood determination unit) is inputted to the first object ID likelihood determination means  104  (first object ID likelihood determination unit). Furthermore, information from the first object position likelihood determination means  103  (first object position likelihood determination unit) and information from the first object ID likelihood determination means  104  (first object ID likelihood determination unit) may also be stored in the first storage unit  110   m . The information from the first object ID likelihood determination means  104  (first object ID likelihood determination unit) is inputted to the object position estimation means  108  (object position estimation unit). In the same manner, information from the second detection unit  102   a  functioning as the second observation unit and the second timer  102   t  may be inputted to the second object position likelihood determination means  105  (second object position likelihood determination unit), and also stored in the second storage unit  111   m . Moreover, information from the second object position likelihood determination means  105  (second object position likelihood determination unit) is inputted to the object tracking state determination means  106  (object tracking state determination unit). Furthermore, information from the second object position likelihood determination means  105  (second object position likelihood determination unit) and information from the object tracking state determination means  106  (object tracking state determination unit) may also be stored in the second storage unit  111   m . Information from the object tracking state determination means  106  (object tracking state determination unit) is inputted to the object position estimation means  108  (object position estimation unit), the association means  109  (association unit) and the second object ID likelihood determination means  107  (second object ID likelihood determination unit). The object position estimation device  99  of  FIG. 1B  having this structure is also allowed to exert the same functions and effects as those of the means (units) corresponding to the object position estimation system in accordance with the first embodiment of  FIG. 1A . 
       FIG. 3  shows a room  301  serving as a specific example of a life environment that is one example of an environment, which is provided with the first observation device  101  and the second observation device  102  that are components of the object position estimation system in accordance with the first embodiment of the present invention. Onto different corners of a ceiling of the square room  301 , tag readers  304  of UWB (Ultra Wide Band) and stereo cameras  305  are respectively attached. Each tag reader  304  functions as one example of the first detection unit  101   a  of the first observation device  101 , and each stereo camera  305  functions as one example of the second detection unit  102   a  of the second observation device  102 . Suppose that a person  302 A having a tag  303 A, a person  302 B having a tag  303 B, and a person  302 C having a tag  303 C who are respectively detectable by the tag reader  304  are present in the room  301 . Hereinafter, a desired person among the person  302 A, the person  302 B, and the person  302 C who are precept in the room  301  is typically explained as a person  302 . In the same manner, a desired tag among the tag  303 A, the tag  303 B, and the tag  303 C, located in the room  301 , is typically explained as a tag  303 . 
     Referring to  FIGS. 1A and 3 , the following description will discuss the respective components. 
     &lt;Explanation of Observation Devices&gt; 
     Both of the first observation device  101  and the second observation device  102  respectively detect persons  302  who are present in the room  301  that is a specific example of a life environment. 
     &lt;Explanation of First Observation Device&gt; 
     The first observation device  101  is provided with the first detection unit  101   a  functioning as a first observation unit, the first object position likelihood determination means  103 , the first object ID likelihood determination means  104 , and the first inner storage unit  110 . The first observation device  101  determines the first ID likelihood (first object ID likelihood) and the first position likelihood (first object position likelihood) of a person  302  who is present in the room  301 . Pieces of information of the first ID likelihood and the first position likelihood of the person  302 , thus determined, are outputted from the first observation device  101  to the object position estimation means  108 . 
     The ID likelihood, mentioned as the first ID likelihood and the second ID likelihood to be described later, refers to a value of probability as to which object ID a detected object (for example, a person  302  in this case) is likely to have (for example, a person  302  in this case). For example, (in the case of radio tags, IDs can be positively identified as described in the prior art section) in the case when a tag of an object A is detected by using a radio tag, the ID likelihood provides the probability of being the object A as 1, while providing the probability of being another object as 0. In contrast, in the case when an object A is detected by using a camera, it is not possible to positively identify the fact that it corresponds to a specific object. For example, even when an object A is identified by a camera, there is the possibility that an object other than the object A (object B, or object C) might be identified. For this reason, the ID likelihood is assigned to all the objects that are present as rates of probability, such as, for example, the probability of being object A is 0.8, the probability of being object B is 0.1, and the probability of being object C is also 0.1. Additionally, this is one example for determining the ID likelihood, and the present invention is not intended to be limited by this. In the first embodiment of the present invention, the first ID likelihood of the person  302  detected by the first detection unit  101   a  of the first observation device  101  is determined by the first object ID likelihood determination means  104 . In contrast, a camera  305 , which is one example of the second detection unit  102   a  of the second observation device  102 , does not have an ID identifying function for objects. For this reason, by using a second object ID likelihood determination means  107  that is different from the second observation device  102 , a second ID likelihood of the person  302  detected by the camera  305  and an image processing unit  102   b  that serve as a second detection unit  102   a  of the second observation device  102 , is determined. 
     The position likelihood, mentioned as the first position likelihood and the second position likelihood to be described later, refers to a value of probability as to which object ID a newly observed object is likely to have, with respect to the position of the observed object, based upon positions of respective objects estimated at a certain point of time. For example, suppose that an object A is located at a position 10, an object B is located at a position 20, and an object C is located at a position 40, respectively on one dimensional coordinate. In this case, suppose that an object is detected at a position 0. At this time, the position likelihood can be calculated by getting a reciprocal of a distance from each of estimated positions of the objects A, B, and C, and then carrying out a normalization operation; thus, the probability of being the object A is calculated as 0.58, the probability of being the object B is calculated as 0.28, and the probability of being the object C is calculated as 0.14. 
     The following description will discuss the object position estimation system at the time of its system activation. In the case when, upon activation of the object position estimation system, the initial estimation position of a person is randomly determined, the tag reader  304  can positively identify the ID of a person. For this reason, it is highly possible that the detected position of a person  302  (by a tag  303  possessed by the person) is closer to the actual position of the person  302  than the (initial) estimated position of the person randomly determined. Therefore, the second position likelihood may be found not based upon the estimated position of the object, but based upon the detected position of the person  302  by the tag reader  304  (using the tag  303  possessed by the person). 
     Moreover, up to observations of the camera  305  of N-number of times (N is an arbitrary integer greater than 0) from the activation of the object position estimation system, the second position likelihood may be determined based upon the detected position of the person  302  by the tag reader  304  (using the tag  303  possessed by the person), and in observations of the camera  305  after the N-th observation of the camera  305 , the second position likelihood may be determined based upon the estimated object position. In this case, the number of times N in which the second position likelihood is determined based upon the detected position of the person  302  by the tag reader  304  (using the tag  303  possessed by the person) becomes different in its optimal value depending on performances of the observation device. For this reason, it is necessary to preliminarily estimate the optimal value by using preliminary experiments or the like. Additionally, this is one example for determining the position likelihood, and the present invention is not intended to by limited by this method. The first position likelihood is determined by the first object position likelihood determination means  103  of the first observation device  101 . The second position likelihood is determined by the second object position likelihood determination means  105  of the second observation device  102 . 
     As the first detection unit  101   a  of the first observation device  101 , for example, the tag reader  304  may be used. 
     The first object position likelihood determination means  103  determines the first position likelihood (the first object position likelihood) of the person  302  detected by the first detection unit  101   a  of the first observation device  101 . In the case when, for example, the tag reader  304  is used as the first detection unit  101   a  of the first observation device  101 , the first object position likelihood determination means  103  can determine the first position likelihood of the person  302  by using the principle of trilateration. More specifically, the installation position of each of the radio tag readers is preliminarily stored in the inner storage unit in the first object position likelihood determination means  103 , or in the first inner storage unit  110 , or the like. Then, the first object position likelihood determination means  103  draws a spherical body centered on the installation position of each radio tag reader, based upon the position at which the tag  303  is detected by each radio tag reader. More specifically, the first object position likelihood determination means  103  is allowed to draw a spherical surface, with a distance, calculated by the detected position and the installation position, serving as a radius. At this time, the first object position likelihood determination means  103  determines a position at which the highest number of spherical surfaces are overlapped with one another as a position at which the tag  303  possessed by the person  302  is located, that is, a position at which the person  302  is present. 
     The first object ID likelihood determination means  104  determines the first ID likelihood (first object ID likelihood) of the person  302  detected by the first detection unit  101   a  of the first observation device  101 . In the case when the tag reader  304  is used as the first detection unit  101   a  of the first observation device  101 , by recording the ID of the person  302  in the tag  303 , the tag reader  304  is allowed to directly read from the tag  303  the ID of the person  302  recorded in the tag  303 . With this arrangement, the first ID likelihood of the person  302  is made to have the probability of being the ID of the person  302  of 1. In the case when no ID of the person  302  is recorded in the tag  303 , for example, a person ID conversion table that allows the determination of the first ID likelihood of the person  302  from the tag ID may be used.  FIG. 4  shows an example of the person ID conversion table. In the person ID conversion table of  FIG. 4 , for example, when a tag corresponding to tag ID=T 4  is detected, this indicates that a person having a person ID=H 1  has been detected. The person ID conversion table may be recorded in the first inner storage unit  110  of the first observation device  101 . Moreover, this may be preliminarily recorded in an external data base or the like, and the first object ID likelihood determination means  104  may acquire necessary information from the external data base, and determine the first ID likelihood of the person  302 . 
       FIG. 5  shows an example of detection of a person by the first observation device  101 .  FIG. 5  is an output example of the tag reader  304  having an observation cycle of 1 second, and an observation ID, a point of time at which the person  302  is detected, a position (xy coordinates) at which the person  302  is detected, and a tag ID possessed by the person  302  are outputted respectively. For example, a person  302  having a tag ID=T 4  is detected at a position (150,210) at a point of time 2008/09/02 — 12:00:00 so that an observation ID=OBS_TAG_ 001  is outputted as first observation information. In the same manner, with respect to tag IDs=T 7 , T 8 , an observation ID=OBS_TAG_ 002  serving as the first observation information and an observation ID=OBS_TAG_ 003  serving as the first observation information are respectively outputted (in the following description, “as the first observation information” is omitted). Additionally, the first observation device  101  is supposed to have a first timer  101   t  used for acquiring pieces of information such as an observation cycle and time. 
     The following explanation will be given by exemplifying a structure in which the tag reader  304  is used as the first detection unit  101   a  of the first observation device  101 . 
     Referring to a flow chart shown in  FIG. 14 , the following description will discuss the contents of processes of the tag reader  304 . 
     In step S 1401 , a tag  303 , located in a room  301  that is a specific example of the environment, is detected by the first detection unit  101   a , and the ID and position of the tag  303  are detected by the first detection unit  101   a . Based upon the position of the tag  303 , the first position likelihood is determined by the first object position likelihood determination means  103 . 
     In step S 1402 , the ID of the person  302  having the tag  303  is detected by the first detection unit  101   a , and the first ID likelihood of the person is determined by the first object ID likelihood determination means  104 . 
     In step S 1403 , the first ID likelihood and the first position likelihood of the person are outputted from the first observation device  101  to the object position estimation means  108 . 
     &lt;Explanation of Second Observation Device&gt; 
     The second observation device  102  is provided with a second detection unit  102   a  functioning as a second observation unit, an image processing unit  102   b , a second object position likelihood determination means  105 , an object tracking state determination means  106 , and a second inner storage unit  111 . In the second observation device, the second position likelihood (second object position likelihood) of a person  302  that is present in a room  301  and information of a tracking state (tracking state information) of the person  302  are acquired by the second observation device  102 , and these pieces of information are respectively outputted to the second object ID likelihood determination means  107 , the object position estimation means  108 , and the association means  109  from the second observation device  102 . For example, a camera  305  may be used as the second detection unit  102   a  of the second observation device  102 . 
     In order to detect the person  302  by using the camera  305 , it is necessary to process image data acquired by the camera  305  serving as one example of the second detection unit  102   a , in the image processing unit  102   b . As the processing method, for example, a background differential method may be used in the image processing unit  102   b . The method is explained as follows: Background image data of an environment where no person  302  is present, for example, the room  301 , which has been preliminarily picked up by the camera  305 , and current image data picked up by the camera  305  are compared with each other in the image processing unit  102   b . Thereafter, a region where pixel values are different is taken out by the image processing unit  102   b  as a differential region. The differential region is detected by the image processing unit  102   b  as being the person  302 . In this case, since there is a possibility that noise is mingled in the image data, the image processing unit  102   b  may determine that the differential region is not the person  302  in the case when the image processing unit  102  has determined that the differential region is sufficiently smaller in comparison with the person  302 . In this case, the state in which the differential region is sufficiently smaller in comparison with the person  302  is considered to be a state in which the number of pixels in the differential region is a threshold value or less, which has been preliminarily set based upon the minimum number of pixels that can be recognized as the person  302 . 
     The second object position likelihood determination means  105  determines the second position likelihood (second object position likelihood) of the person  302  detected by the second detection unit  102   a  and the image processing unit  102   b  in the second observation device  102 . Suppose that the camera  305  serving as one example of the second detection unit  102   a  of the second observation device  102  is installed in such a manner as to look down on the ground vertically from a ceiling. In this case, for example, the second object position likelihood determination means  105  can determine the second position likelihood of the person  302  based upon the center-of-gravity position of the differential region acquired by the image processing unit  102   b.    
     The object tracking state determination means  106  determines information of a tracking state of the person  302  (tracking state information) detected by the second detection unit  102   a  and the image processing unit  102   b  of the second observation device  102 . In the case when a camera  305  serving as one example of the second detection unit  102   a  of the second observation device  102  is used, for example, by storing a color distribution of a differential region acquired by the image processing unit  102   b  in the second inner storage unit  111 , the tracking of the person  302  can be achieved. Referring to a person detection history data base stored in the second inner storage unit  111  shown in  FIG. 6 , the tracking process will be explained.  FIG. 6  shows an output history of the camera  305  having an observation cycle of one second, which records an observation ID, a point of time at which the person  302  has been detected, a position (xy coordinates) at which the person  302  has been detected, and an amount of color features of the person  302 . Second observation information of observation ID=OBS_CAM_ 001  represents the fact that the camera  305  has detected the person  302  at a position (150, 410) at a point of time of 2008/09/02 — 12:00:00. Moreover, red is recorded as the amount of color features, and as a result of an analysis of the color distribution of the differential region by the image processing unit  102   b , it is indicated that the component of a red color is the largest. Second observation information of observation ID=OBS_CAM_ 004  (hereinafter, “observation ID=” and “of the second observation information” are omitted), which corresponds to a detection one second thereafter, also has red recorded in its amount of color features. At this time, since the amount of color features is the same, it is estimated that the person  302  detected at OBS_CAM_ 004  is the same person as a person  302  detected at OBS_CAM_ 001 . For this reason, the object tracking state determination means  106  determines that OBS_CAM_ 004  is tracking state information obtained by tracking OBS_CAM_ 001 . With respect to the person  302  detected at OBS_CAM_ 001 , since no observation ID having the same amount of color features is previously present, the object tracking state determination means  106  determines the fact that the person is first detected as its tracking state information. In this case, as the amount of color features, an RGB component ratio or the like may be used. In this case, the second observation device  102  is supposed to have a second timer  102   t  used for acquiring an observation cycle and information of time. 
     The following description will discuss a state in which no OBS_CAM_ 004  is obtained. In the previous observation in which OBS_CAM_ 007  was obtained, no observation value having the same amount of “red” color feature as OBS_CAM_ 007  was obtained. In the case when the detection performance of the person of the camera  305  is high, the object tracking state determination means  106  may determine that OBS_CAM_ 007  corresponds to a person detected for the first time. In contrast, in the case when the detection performance of the person of the camera  305  is low, by determining that the detection of the person was erroneously made in the previous observation obtaining OBS_CAM_ 007 , the object tracking state determination means  106  may determine that OBS_CAM_ 001  which had the same amount of “red” color feature in the previous observation value corresponds to the same person as OBS_CAM_ 007 . 
     In the above example, the object tracking state determination means  106  has determined as its tracking state information that the person  302  detected as OBS_CAM_ 004  is the same person  302  detected as OBS_CAM_ 001 , However, even when the same amount of color feature is detected as OBS_CAM_ 001  and as OBS_CAM_ 004 , the object tracking state determination means  106  sometimes determines that respectively different persons have been detected based upon a difference in the detection times and a difference in the detection positions. For example, in the case when it is clear that a movement from the detected position (150, 401) of OBS_CAM_ 001  to the detected position (320, 390) of OBS_CAM_ 004  is never achieved by a person&#39;s walking speed within a difference between detected times, such a determination is made. As such a determination criteria, for example, a case is considered in which the distance between the detected position (150, 401) and the detected position (320, 390) is greater than a possible walking distance given by a product of the period of time and the person&#39;s highest walking speed (for example, 5 meters per second). 
     In the above example, however, in the case when two persons having the same amount of color feature pass each other, it is not possible for the object tracking state determination means  106  to determine which previously obtained observation ID the observation ID obtained at this time is derived from through its tracking process. 
     In this case, the object tracking state determination means  106  may take person&#39;s movement models into account.  FIG. 17  shows an observation state of the camera  305  when two persons respectively having the same amount of color feature pass each other. Additionally, observation values of the tag reader  304  are supposed to be also obtained; however, those values are omitted in this case. 
     As described earlier, at time 12:00:02, the object tracking state determination means  106  determines that OBS_CAM_ 103  serving as tracking state information is an observation value obtained by tracking OBS_CAM_ 101  that has the same amount of color feature, and is capable of reaching within one second. In the same manner, the object tracking state determination means  106  also determines that OBS_CAM_ 104  is an observation value obtained by tracking OBS_CAM_ 102  that has the same amount of color feature, and is capable of reaching within one second. 
     In this case, not only the amount of feature, but also the period of time during which the object is present is taken into account because of the following reasons: If observation IDs can be mutually associated with each other as pieces of tracking state information by using only the amount of feature of objects and can also be dealt as pieces of tracking state information, it is quite simple. However, actually, it is supposed that there are cases in which a plurality of objects having the same amount of feature are present for example, there is a case in which persons wearing the same clothes are present). In such a case, not only the amount of feature, but also the position at which the object is present for each point of time may be taken into account so as to associate observation IDs with one another. Rules may be preliminarily determined in which, for example, when persons are moving at places 100 m or more apart from each other within one second, they are not associated with each other even when they have the same amount of feature, and these rules may be stored and applied to the object tracking state determination means  106 . 
     Based upon these, the object tracking state determination means  106  determines that OBS_CAM_ 103  is advancing in −x-direction at 2 m per second and that OBS_CAM_ 104  is advancing in +x-direction at 2 in per second. 
     At time 12:00:03, there are two observation values OBS_CAM_ 103  and OBS_CAM_ 104  that have the same amount of color feature and are capable of shifting within one second as observation values serving as tracking origins for OBS_CAM_ 105 . At this time, the object tracking state determination means  106  refers to pieces of information of advancing history of OBS_CAM_ 103  and OBS_CAM_ 104  of the person detection history data base of the second inner storage unit  111  so that the object tracking state determination means  106  determines which observation value corresponds to an observation value of the tracking origin for OBS_CAM_ 105 . Since OBS_CAM_ 103  was advancing in the −x-direction at 2 m per second, the object tracking state determination means determines that the possibility that the next observation (observation at time 12:00:03) can be obtained at coordinates (550, 350) is high. Moreover, since OBS_CAM_ 104  was advancing in the +x-direction at 2 m per second, the object tracking state determination means determines that the possibility that the next observation can be obtained at coordinates (450, 250) is high. 
     In this case, the object tracking state determination means  106  can determine that OBS_CAM_ 105  was obtained at coordinates (550, 350), and corresponds to an observation value obtained by tracking OBS_CAM_ 103 . In the same manner, the object tracking state determination means  106  can determine that OBS_CAM_ 106  corresponds to an observation value obtained by tracking OBS_CAM_ 104 . 
     Additionally, as described earlier, the person detection history data base may be recorded in the second inner storage unit  111  of the second observation device  102 . Moreover, this data base may be recorded in an external data base or the like, and the object tracking state determination means  106  may acquire necessary information from the external data base or the like, if necessary, so as to obtain information of the object tracking state. Additionally, the observation cycles of the first observation device  101  and the second observation device  102  are not necessarily made the same. 
     The following explanation will be given by using a camera  305  as one example of the second detection unit  102   a  of the second observation device  102 . 
     Referring to a flow chart of  FIG. 15 , the following description will discuss the processing contents of the camera  305 . 
     In step S 1501 , the second detection unit  102   a  detects a person  302  that is present in a room  301  serving as a specific example of the environment, and based upon the position detected by the image processing unit  102   b , the second object position likelihood determination means  105  determines the second position likelihood. 
     In step S 1502 , pieces of information detected by the second detection unit  102   a  and the image processing unit  102   b  are recorded in the person detection history data base of the second inner storage unit  111  by the second detection unit  102   a  and the image processing unit  102   b.    
     In step S 1503 , the object tracking state determination means  106  refers to the person detection history data base so that the tracking state of the person  302  detected by the second detection unit  102   a  and the image processing unit  102   b  is determined by the object tracking state determination means  106 . 
     In step S 1504 , the second position likelihood and the tracking state information are outputted to the second object ID likelihood determination means  107 , the object position estimation means  108 , and the association means  109 , respectively, from the second observation device  102 . 
     In this case, as the observation cycle of the first observation device  101  and the second observation device  102  becomes shorter, the number of times for object position estimations increases so that the position estimation precision becomes higher. 
     &lt;Explanations of Association Means and Object Position Estimation Means&gt; 
     Based upon the first ID likelihood and the first position likelihood of the person  302  determined (detected) by the first observation device  101 , the second position likelihood of the person  302  determined (detected) by the second observation device  102 , and the second ID likelihood determined by the second object ID likelihood determination means  107 , the object position estimation means  108  estimates the position of the person  302 . 
     More specifically, the following method is used. Upon receipt of the first ID likelihood and the first position likelihood described below as the method for estimating the position, the association means  109  calculates an association value based upon the first ID likelihood and the first position likelihood of the object, thus received. 
     Based upon the first ID likelihood and the first position likelihood of the object received from the first observation device  101 , the second position likelihood of the object received from the second observation device  102 , and the second ID likelihood of the object received from the second object ID likelihood determination means  107 , the association means  109  finds which object ID the detected object has, on the basis of probability. The detection of the object is carried out by the first detection unit  101   a  of the first observation device  101 , and the second detection unit  102   a  and the image processing unit  102   b  of the second observation device  102 . In this explanation, the first observation device  101  and the second observation device  102  are collectively referred to as “observation devices”, the first ID likelihood and the second ID likelihood are collectively referred to as “ID likelihoods”, and the first position likelihood and the second position likelihood are collectively referred to as “position likelihoods”. 
     Here, the probability value found by the association means  109  is referred to as “association value”. More specifically, the association value is a value that indicates a relationship between pieces of information of the ID likelihood and position likelihood observed by the observation device and an actual object (with respect to the second ID likelihood, its output is obtained by the second object ID likelihood determination means). That is, the value represents probability as to which object ID is detected to obtain values corresponding to the ID likelihood and position likelihood received by the observation device. The ID likelihood and the position likelihood of the object, described earlier, correspond to values that represent certainty as to the fact that the respective pieces of observation information (observation data) are information (data) obtained by observing a certain object. Moreover, the association value is represented by a product of the ID likelihood and the position likelihood. Additionally, the second ID likelihood of the object, detected by the second observation device  102  to be used for calculating the association value in the association means  109 , is calculated not by the second observation device  102 , but by the second object ID likelihood determination means  107 . 
     Upon completion of calculations of the association value by the association means  109 , the object position estimation means  108  estimates the position of the person  302  based upon the calculation results of the association means  109  and the tracking state information of the object. Additionally, the completion of the calculations of the association value by the association means  109  can be determined by the input of the calculation results in the association means  109  into the object position estimation means  108  from the association means  109 . When the information relating to the calculation results is inputted to the object position estimation means  108  from the association means  109 , the object position estimation means  108  determines that the calculations of the association value have been completed. Then, based upon the calculation results in the association means  109  and the tracking state information of the object, the object position estimation means  108  estimates the position of the person  302 . 
     As the estimating method, the frame work of Bayesian estimation typically represented by a Kalman filter may be used in the object position estimation means  108 . Based upon the ID likelihood and position likelihood of the person  302  obtained from the second observation device  102 , the position estimation is carried out by the object position estimation means  108 , and at this time, the amount of update of the position of the person  302  is weighted by the association value. Information relating to the weighting process by the association value is outputted from the association means  109  to the object position estimation means  108 . 
     In this case, with respect to the association value, the higher the value, the greater the amount of update of the object position becomes. In other words, observation data having high probability of being observation data for a certain object have a higher rate of devotion to updating the position of the observation data. 
     &lt;Explanation of Kalman Filter&gt; 
     The Kalman filter is a filter used for estimating a system state having the highest likelihood on the assumption that information of a system state (for example, the object position in the first embodiment of the present invention) and both of the observation data (observation information) of the first observation device  101  and the second observation device  102  contain noise. In other words, among the candidates that can be taken by the system, the state having the highest probability is estimated. 
       FIG. 2  shows an example in which a Kalman filter is used for the object position estimating process. The axis of ordinates represents the probability, and the axis of abscissas represents the position. 
     When an object makes a movement as indicated by the following (equation 1), the second observation device  102  can obtain an observation value  203  found by the following (equation 2). In this case, A represents a movement model of the object, x represents an object position, and v represents a process noise generated upon the movement. Moreover, y represents an observation value, H represents an observation model that makes the object position x and the observation value y associated with each other, w represents an observation noise, and t represents time.
 
[Formula 1]
 
 X   t   =A   t   x   t-1   +v   t   (Equation 1)
 
[Formula 2]
 
 y   t   =H   t   x   t   +w   t   (Equation 2)
 
     In this case, supposing that the process noise v and observation noise w are white Gaussian noise, p(w) is indicated by the following (equation 3), and p(v) is indicated by the following (equation 4). In this case, N (0, Q) represents a Gaussian distribution having an average of 0 and a dispersion of Q. In the same manner N (0, R) represents a Gaussian distribution having an average of 0 and a dispersion of R.
 
[Formula 3]
 
 p ( w )˜ N (0 ,Q )  (Expression 3)
 
[Formula 4]
 
 p ( v )˜ N (0 ,R )  (Expression 4)
 
     When the observation value  203  has been obtained, the object position estimation means  108  updates a preliminary probability distribution  201  (hereinafter, referred to as “preliminary distribution”) relating to the position of the object currently obtained so that the object position estimation means  108  forms a predicted probability distribution  202  (hereinafter, referred to as “predicted distribution”). The object position estimation means  108  finds the average (position) of the predicted distribution  202  based upon (equation 5), and the object position estimation means  108  also finds the dispersion of the predicted distribution  202  based upon (equation 6). Additionally, X a|b , represents an estimated value of X at time a based upon information at time b. For example, “x t|t-1 ” of (equation 5) represents an estimated value of an object position x at time t based upon information at time t−1, and “P t|t-1 ” of (equation 6) represents an estimated value of P at time t based upon information at time t−1. In this case, P represents the dispersion of the distribution.
 
[Formula 5]
 
 x   t|t-1   =A   t   X   t-1|t-1   (Expression 5)
 
[Formula 6]
 
 P   t|t-1   =A   t   P   t-1|t-1   A   t   T   +Q   t   (Expression 6)
 
     When the predicted distribution  202  has been obtained by the object position estimation means  108 , the object position estimation means  108  finds a post distribution  204  from the observation value  203  and the predicted distribution  202 . The average (position) of the post distribution is found by the object position estimation means  108  from (equation 7), and the dispersion of the post distribution can be found by the object position estimation means  108  from (equation 8). In this case, K represents a value referred to as a Kalman gain, which is found by (equation 9). The Kalman gain is a value used for determining the amount of update. In the case when the precision of the observation value is good (the dispersion R is very small), the value of the Kalman gain becomes greater so as to increase the amount of update. In contrast, in the case when the precision of the preliminary distribution is good (the dispersion P is very small), the value of the Kalman gain becomes smaller so as to reduce the amount of update. 
     Additionally, in the case when the amount of update of the position of an article A is weighted by using the association value, as will be described later, equation (9) can be replaced by equation (9A). In equation (9A), “D” represents an association value relative to the article A.
 
[Formula 7]
 
 x   t|t   =x   t|t-1   +K   t ( y   t   −H   t   x   t|t-1 )  (Expression 7)
 
[Formula 8]
 
 P   t|t =(1− K   t   H   t ) P   t|t-1   (Equation 8)
 
[Formula 9]
 
 K   t   =P   t|t-1   H   t   T ( H   t   P   t|t-1   H   t   T   +R   t ) −1   (Equation 9)
 
 K   t   =P   t|t-1   H   t   T ( H   t   P   t|t-1   H   t   T   +R   t   /D   ObjectA ) −1   (Equation 9A)
 
       FIG. 7  shows an example of an output history of the object position estimation means  108 . In this case, it is supposed that the object position estimation means  108  confirms a positional error characteristic, such as a standard deviation relating to positions between the first observation device  101  and the second observation device  102 . In other words, it is supposed that the information of the positional error characteristic, such as the standard deviation relating to the positions between the first observation device  101  and the second observation device  102 , is inputted to the object position estimation means  108 , and stored in the inner storage unit or the like of the object position estimation means  108 . 
     &lt;Explanation of Second Object ID Likelihood Determination Means&gt; 
     Based upon the object tracking state information of the object tracking state determination means  106  and the association value found by the association means  109 , the second object ID likelihood determination means  107  determines the second ID likelihood (second object ID likelihood) in the observation value observed by the camera  305  and the image processing unit  102   b.    
     When the observation value observed by the camera  305  and the image processing unit  102   b  is an observation value that has been first observed, the second object ID likelihood determination means  107  determines that the observation value has the possibility of corresponding to any one of persons that are all present in a room  301 . That is, in the case when three persons, that is, a person  302 A, a person  302 B, and a person  302 C, are present in the room  301 , the second object ID likelihood determination means  107  determines that the observation value observed by the camera  305  and the image processing unit  102   b  is obtained by observing the three persons with the same probability (1/3). More specifically, the probability of the observation value that is obtained by detecting person ID=H 1  (for example, the person  302 A) is 1/3, the probability of the observation value that is obtained by detecting person ID=H 2  (for example, the person  302 B) is 1/3, and the probability of the observation value that is obtained by detecting person ID=H 3  (for example, the person  302 C) is 1/3; thus, the second ID likelihoods of the persons are respectively determined. As described earlier,  FIG. 7  shows an example of an output view showing the results of estimations by the object position estimation means  108 , and the estimation position (estimated position) is calculated based upon the preliminary estimated position of the person and the observation value of the observation device. In this case, in the estimation results at time 12:00:00, since the preliminary estimation position of the person is the initial value (in this example, the center of the room), it is difficult to estimate the person&#39;s position at a right position, even when calculations are carried out, with the observation value of the observation device being included. By successively updating the position of the person each time an observation value of the observation device is obtained, for example, at time 12:00:001, time 12:00:002, . . . , and so on, the estimation precision of the person&#39;s position is improved, and this can be understood by  FIG. 7 . 
     By using the second ID likelihood determined by the second object ID likelihood determination means  107 , as described above, the association value is found by the association means  109 , based upon the person&#39;s ID and the position of the person that is being currently estimated. The association value, thus found, is stored in an association value data base  109   a  of the inner storage unit that is built in the association means  109 . 
       FIG. 8  shows an example of the association value data base  109   a . In the association value data base  109   a , the observation ID of the camera  305  serving as one example of the second detection unit  102   a  of the second observation device  102  and association values relative to the respective persons  302  are recorded. In accordance with  FIG. 8 , the person detected as OBS_CAM_ 004  has the highest probability of 0.69 of being the person HUM_ 001 , the next highest probability of 0.19 of being the person HUM_ 002 , and the lowest probability of 0.12 of being the person HUM_ 003 . With respect to the other observation IDs, association values relating to the respective persons are recorded in the same manner. In this case, the association value data base  109   a  may be recorded in the inner storage unit of the association means  109 . Moreover, this may be recorded in an external data base or the like, and the association means  109  may acquire necessary information from the external data base, if necessary, and then calculate association values. 
     In the case when the observation value observed by the camera  305  and the image processing unit  102   b  is an observation value obtained by tracking the previous observation value, the second object ID likelihood determination means  107  determines the previous association value found by the association means  109  based upon the previous observation value associated with the above-mentioned observation value as the second ID likelihood. Then, by using the second ID likelihood determined by the second object ID likelihood determination means  107 , the association means  109  finds the current association value based upon the person&#39;s ID and the position of the person that is being currently estimated. The association value thus found is recorded in the association value data base  109   a  by the association means  109 . 
     The following description will discuss the possibility of the camera  305  failing to track the observation value. Even when the camera  305  outputs from the second observation unit  102  to the second object ID likelihood determination means  107  an observation value as the result of tracking the same object by making the previous observation value associated with the observation value currently obtained, there is a possibility that, actually, an observation value obtained by observing another person appeared for the first time might be detected by the camera  305  and the image processing unit  102   b  due to influences of noise or the like. Therefore, by introducing a probability variable T that allows the camera  305  to correctly track the observation value to the second object ID likelihood determination means  107 , the second ID likelihood determination means  107  may determine the second ID likelihood in accordance with (equation 10). 
     In (equation 10), p id  represents a second ID likelihood, r represents an association value, N represents the number of persons present in the room  301 , and t represents time. 
     It is indicated by (equation 10) that, when the probability variable T=0, the tracking positively causes a failure, or it is considered that an observation value is first detected, so that the second ID likelihood p id  of the observation value is represented by (equation 11). 
     Moreover, when the probability variable T=1, the tracking is considered to be positively successful so that the second ID likelihood p id  of the observation value is represented by (equation 12). 
     That is, in other words, the object tracking state determination means  106  outputs a tracking success likelihood that indicates the probability of successful tracking of the object and a tracking failure likelihood that indicates the probability of failure in tracking the object. The second object ID likelihood determination means  107  provides a sum of a value obtained by multiplying the association value calculated upon the previous detection of the object by the tracking success likelihood and a value obtained by dividing the tracking failure likelihood by the number of all the objects serving as detection subjects, as the second ID likelihood p id  of the object. In this case, the tracking success likelihood corresponds to the probability variable T, and the tracking failure likelihood corresponds to (1−T). 
     The way how to find the second ID likelihood p id  that forms the operation result of the second object ID likelihood determination means  107  is carried out such that in the case of successful tracking of the object, the ID likelihood of the object is estimated based upon the association value of the previous observation value. However, actually, there is a possibility of a failure in tracking the object. Therefore, the estimated ID likelihood is assigned by a portion corresponding to the probability of the tracking success likelihood, based upon the association value of the previous observation value. This corresponds the left side (r t − 1 T t ) of (equation 10). 
     In contrast, in the case of the failure in tracking the object, since no estimation is obtained as to which ID the object has; therefore, since there is a possibility that the ID corresponds to any one of the objects, the probability is evenly assigned. This means that the evenly assigning process is carried out for the portion of the tracking failure likelihood (1−T). This corresponds the right side {(1−T t )/N} of (equation 10) (where N=the number of objects). In the case of the successful tracking of the object, since the probability variable T=1, the value of the right side {(1−T t )/N} of (equation 10) becomes 0, with the value of the left side (r t − 1 T t ) of (equation 10) being set to r t-1 , so that (equation 12) is obtained. In the case of the positive failure of the tracking, T=0, and the value of the left side (r t − 1 T t ) of (equation 10) becomes 0, with the value of the right side {(1−T t )/N} of (equation 10) being set to {1/N}, so that (equation 11) is obtained. 
     With respect to the way how to find the probability variable T in the second object ID likelihood determination means  107 , for example, a method is proposed in which, preliminary experiments are carried out so that a probability of actual successful tracking is determined, and the resulting information is stored in the inner storage unit of the second object ID likelihood determination means  107 . Moreover, in the case when, as described in the object tracking method of the second observation device  102 , a plurality of observation values that cannot be identified by the amount of color feature or the like are closely located with one another, with the result that the probability of a failure in tracking becomes higher, the second object ID likelihood determination means  107  may carry out, for example, such a process as to lower the value of the probability variable T. In this case, as to whether or not an actual case corresponds to “the case in which a plurality of observation values that cannot be identified by the amount of color feature or the like are closely located with one another”, the second object ID likelihood determination means  107  may make a determination, for example, in the following manner. In the case when, supposing that the sensor carries out an observation once every one second, there are three persons or more (who are being subjected to estimation processes) within a distance in which the person can proceed per one second (for example, a circle with a radius of 3 m), the second object ID likelihood determination means  107  determines that the closely located state occurs within the distance. Moreover, in the case when all the three persons have the same amount of color feature, it may carry out, for example, such a process as to further lower the value of the probability variable T. 
     
       
         
           
             
               
                 
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     Furthermore, the following description will discuss a state where two persons are overlapped with each other on the camera screen.  FIG. 12A  shows a position of a person having a person ID=H 1  (hereinafter, referred to simply as “person of H 1 ”) and a position of a person having a person ID=H 2  (hereinafter, referred to simply as “person of H 2 ”), detected by the camera  305  and the image processing unit  102   b  at time T, as well as a distance between the detected positions of person of H 1  and person of H 2 .  FIG. 12B  shows a position of person of H 1  and a position of person of H 2 , detected by the camera  305  at time T+1, as well as a distance between the detected positions of person of H 1  and person of H 2 . The object tracking state determination means  106  determines that from time T to time T+1, the distance between person of H 1  and person of H 2  is shortened from 600 cm to 200 cm. In this case, it is supposed that the camera  305  is allowed to follow person of H 1  and person of H 2  by the object tracking state determination means  106 . Moreover,  FIG. 13  shows a person detection state by the camera  305  at time T+2.  FIG. 13  shows that at time T+2, only person of H 1  is detected. This is considered to be caused by the fact that, by a blind spot generated by the presence of person of H 1 , person of H 2  is concealed. At this time, the variable r of equation 10 may be set to the average value between the association value of person of H 1  at time T+1 and the association value of person of H 2  at time T+1. Additionally, by taking it into consideration the possibility that person of H 2  has gone out of the room  301 , the second object ID likelihood determination means  107  may add the association value of person of H 2  obtained by carrying out a weighting process on the association value of person of H 1  at time T+1 to the second ID likelihood of person of H 1 . Moreover, by setting a threshold value to the distance between the detected positions of the persons, the second object ID likelihood determination means  107  can determine whether or not person of H 2  is concealed by a blind spot generated by the presence of person of H 1 . That is, with respect to the detected position A of person of H 1 , in the case when the second object ID likelihood determination means  107  determines that a detected position B of person of H 2  is located within a distance shorter than the threshold value, if person of H 1  is not detected, the second object ID likelihood determination means  107  determines that person of H 1  is concealed by person of H 2 . With respect to person of H 2  or the other persons, the second object ID likelihood determination means  107  makes the same determination. 
     &lt;Explanation of Relationship Between Object Position Estimation Means and Second Object ID Determination Means&gt; 
     Referring to  FIGS. 9A to 11F , the following description will explain the object position estimation means  108  and the second object ID likelihood determination means  107 . 
       FIGS. 9A to 9C  show actual positions of person  302 A, person  302 B, and person  302 C that are present in a room  301 .  FIG. 9A  shows actual positions of person  302 A, person  302 B, and person  302 C at time 2008/09/02 — 12:00:00.  FIG. 9B  shows actual positions of person  302 A, person  302 B, and person  302 C at time 2008/09/02 — 12:00:01.  FIG. 9C  shows actual positions of person  302 A, person  302 B, and person  302 C at time 2008/09/02 — 12:00:02. In accordance with  FIG. 9A , person  302 A of personal ID=H 1  (hereinafter “personal ID=” is omitted) was actually present at a position of coordinates (190, 380) at time 2008/09/02 — 12:00:00. In this case,  FIG. 9  are views that explain the object position estimation means  108  and the second object ID likelihood determination means  107 , and information indicated in  FIG. 9A  is not used for the other explanations of the first embodiment of the present invention. 
       FIGS. 10A to 10C  show detected states of persons by the tag reader  304  and the camera  305  in the room  301 .  FIG. 10A  shows detected states of persons at time 2008/09/02 — 12:00:00.  FIG. 10B  shows detected states of persons at time 2008/09/02 — 12:00:01.  FIG. 10C  shows detected states of persons at time 2008/09/02 — 12:00:02. In accordance with  FIG. 10A , three detected positions  1001  of persons are obtained by the tag reader  304 , and person  302 A of tag ID=T 4  (hereinafter, “tag ID=” is omitted, and referred to simply as “tag”) is detected at a position of coordinates (150, 210), person  302 B of tag T 7  is detected at a position of coordinates (620, 100), and person  302 C of tag T 8  is detected at a position of coordinates (620, 630), respectively (see  FIG. 5 ). In the same manner, three camera detected positions  1002  of persons are obtained, and one person is detected at a position of coordinates (150, 410), one person is detected at a position of coordinates (810, 220), and one person is detected at a position of coordinates (810, 220), respectively (see  FIG. 6 ). 
       FIGS. 11A to 11F  show positions of persons  302  estimated by the object position estimation means  108  in the room  301 .  FIG. 11A  shows the initial position of the estimated position upon activation of the object position estimation system of the first embodiment of the present invention at time 2008/09/02 — 12:00:00. Although the initial position is determined at random in  FIG. 11A , it may be set to the center or the like of the room  301 . In the case when the object position estimation system has an environment map (environment map information) in which entrance information or the like of the position of an entrance of the room  301  or the like is described, the entrance of the room  301  may be used as the initial position. In addition to these, the environment map (environment map information) may include blind-spot information of the first observation device or the blind-spot information of the second observation device. In the case when only the entrance information is used for setting the initial position, the environment map (environment map information) having the entrance information recorded therein can be stored in the inner storage unit of the object position estimation means  108 . Moreover, an environment map (environment map information) having camera blind-spot information recorded therein, which is referred to by the object tracking state determination means  106 , can be stored in the inner storage unit of the second observation device  102 . 
       FIGS. 19A and 19B  show examples of the environment map. The environment map includes entrance environment map data in which position coordinates information of an entrance is recorded as shown in  FIG. 19A , and camera blind spot environment map data in which blind spot information of the camera is recorded as shown in  FIG. 19B . The entrance environment map is stored and recorded in the inner storage unit of the object position estimation means  108 , and the camera blind spot environment map is stored and recorded in the inner storage unit of the camera  305 . 
     In the case when the entrance of the room  301  is set as the initial position, the object position estimation means  108  refers to the entrance environment map so that the object position estimation means  108  sets the initial position as (x,y)=(100, 0). 
     The blind spot of the camera  305  is represented as a rectangular shape having two points recorded in the camera blind spot environment map as orthogonal points. 
     The following description will discuss a method for utilizing the blind spot of the camera  305 . 
     Suppose that “observation value A” having an amount of red-color feature is obtained at a position (x,y)=(98,99) at time 13:00:00. Moreover, suppose that no observation value having an amount of red-color feature is obtained at time 13:00:01 and time 13:00:02, and that at time 13:00:03, “observation value B” having an amount of red-color feature is obtained at a position (x,y)=(201,202) at time 13:00:03. 
     In the case when the ID likelihood of the observation value B is obtained in this state, since no observation value having the same amount of feature as that of the observation value B in the previous observation (13:00:02), the observation value B is determined as an observation value that has been first observed. That is, as represented by (equation 11), the ID likelihood is evenly assigned to the IDs of all the persons. 
     Upon reference to the camera blind spot environment map by the object tracking state determination means  106 , it is found that both of the observation value A and observation value B are located close to “camera blind spot  1 ”. That is, the object tracking state determination means  106  determines that there is a possibility of having no observation value because the person enters the blind spot of the camera at time 13:00:01 and time 13:00:02. Therefore, the observation value B is determined by the object tracking state determination means  106  not as an observation value that has been first observed, but as an observation value obtained by tracking the observation value A. 
     Additionally, with respect to the method for making a determination as to whether or not an observation value is located close to the camera blind spot, the object tracking state determination means  106  may utilize a relationship between an observation cycle of the camera and a walking speed of the person. For example, supposing that the camera observation cycle is once per second and that the walking speed of the person is 70 cm per second, an observation value observed within 70 cm from the camera blind spot is determined as an observation value obtained close the camera blind spot by the object tracking state determination means  106 . 
       FIG. 11B  shows estimated positions of persons, estimated based upon person information detected by the tag reader  304  and the camera  305  at time 2008/09/02 — 12:00:00. Moreover,  FIG. 11C  shows the initial positions of estimated positions at time 2008/09/02 — 12:00:01.  FIG. 11D  shows estimated positions of persons, estimated by also using person information detected by the tag reader  304  and the camera  305  at time 2008/09/02 — 12:00:01. Moreover,  FIG. 11E  shows the initial positions of estimated positions at time 2008/09/02 — 12:00:02.  FIG. 11F  shows estimated positions of persons, estimated by also using person information detected by the tag reader  304  and the camera  305  at time 2008/09/02 — 12:00:02. 
     &lt;Explanation of Operations of the Present System&gt; 
     Referring to a flow chart of  FIG. 16 , the following description will discuss the operations in detail along a time flow. Additionally, the following sequence of operations are carried out under operation control by the object position estimation device  120 . Of the object position estimation system, the second object ID likelihood determination means  107 , the object position estimation means  108 , and the association means  109  are supposed to form the object position estimation device  120 . Moreover, the object position estimation system is explained as being composed of the object position estimation device  120 , the first observation device  101  having the tag reader  304 , and the second observation device  102  having the camera  305 . The tag reader  304  is one example of the first detection unit  101   a,  and the camera  305  is one example of the second detection unit  102   a.    
     Upon activation of the object position estimation system of the first embodiment of the present invention, in step S 1601 , first the initial position is set to an estimated position of a person  302  by the object position estimation means  108  (see  FIG. 11A ). Additionally, the object position estimation system of the first embodiment is supposed to have a counter for use in confirming how many times the object positions are updated in the object position estimation means  108 . When this counter is 0, that is, upon the actuation of the object position estimation system in which no update of the object position has been executed, the position of a person  302  is set to the initial position. In this case, it is supposed that a gate-type tag reader  304   d , which functions as a tag reader of the first observation device  101 , is installed at an entrance  301 D of the room  301  indicated by a chain line in  FIG. 3 . Moreover, when the persons  302  enter the room  301  through the entrance  301 D, the tags of all the persons  302  entered the room  301  can be read by the gate-type tag reader  304   d . For this reason, all the IDs of the persons  302  who are present in the room  301  are supposed to be confirmed by the first observation device  101 . Moreover, the IDs of the persons  302  who are present in the room  301  may be recorded, for example, in the first inner storage unit  110  or the like of the first observation device  101  of the object position estimation system. In the case when no gate-type tag reader can be installed due to an environmental reason, the initial positions of the persons  302  may be set to initial positions obtained when the persons  302  have been first detected by the tag reader  304 . 
     Next, in step S 1602 , the object position estimation device  120  is allowed to receive outputs, that is, pieces of observation information, from the first observation device  101  and the second observation device  102 . In this case, for example, suppose that, at time 2008/09/02 — 12:00:00, the object position estimation device  120  received observation information including persons  302  who were first detected by the tag reader  304 , the camera  305 , and the image processing unit  102   b  (see  FIG. 10A  with respect to the detection states, and see  FIGS. 5 and 6  with respect to the positional coordinates detected). The pieces of information detected by the tag reader  304  and the camera  305  are outputted to the object position estimation device  120 . In this example, an explanation is given by exemplifying a state in which both of the observation times of the tag reader  304  and the camera  305  are set to 2008/09/02 — 12:00:00; however, actually the observation times might have a deviation. In such a case, for example, by setting reference times with one-second intervals, and the system may determine all the observation values obtained within a permissible range from the reference time, for example, within a range of ±500 msec, as the observation values obtained at the same time.  FIG. 18  shows one example of the setting of the reference times. In the observation state of  FIG. 18 , the observations are carried out within a range of ±500 msec from 12:00:01:000 that corresponds to the reference time so that OBS_CAM_ 201  and OBS_TAG_ 201  are determined by the system as being observed at the same time. In the same manner, OBS_CAM_ 202  and OBS_TAG_ 202 , OBS_CAM_ 203  and OBS_TAG_ 203  are determined respectively by the system as being observed at the same time. 
     Next, in step S 1603 , pieces of person detection information (OBS_CAM_ 001 , OBS_CAM_ 002 , OBS_CAM_ 003 ), detected by the camera  305  and the image processing unit  102   b  of the second detection unit  102   a  of the second observation device  102 , contain no second ID likelihood required for the process of the object position estimation means  108 . For this reason, first, the person detection information is outputted to the second object ID likelihood determination means  107  from the second observation device  102 , and in the second object ID likelihood determination means  107 , the second ID likelihood of the person  302  detected in the second observation device  102  is determined. In this case, in step S 1603 , it is determined whether or not the person detection information has been outputted from the second observation device  102  to the second object ID likelihood determination means  107 . In the case when the person detection information has been outputted from the second observation device  102  to the second object ID likelihood determination means  107 , the sequence proceeds to step S 1604 . In contrast, in the case when the person detection information is not outputted from the second observation device  102  to the second object ID likelihood determination means  107 , the sequence proceeds step S 1609 . In this example, since the camera  305  outputs the person detection information, the sequence proceeds to the direction of YES in step S 1603 , thereby proceeding to step S 1604 . In contrast, when no person detection information is outputted from the camera  305 , for example, when the person  302  enters the blind spot of the camera  305 , the sequence proceeds to the direction of NO in step S 1603 , thereby proceeding to step S 1609 . 
     In step S 1604 , the second ID likelihood of the person detected by the second observation device  102  is determined by the second object ID likelihood determination means  107 . That is, based upon the output from the object tracking state determination means  106  of the second observation device  102  to the second object ID likelihood determination means  107 , the second object ID likelihood determination means  107  determines that pieces of information of OBS_CAM_ 001 , OBS_CAM_ 002 , and OBS_CAM_ 003  are person detection information obtained by detecting new persons. The reason for this is because, as described earlier, when no information obtained by detecting those persons exists within a certain period of time prior to those pieces of information, the second object ID likelihood determination means  107  is allowed to make such a determination. Moreover, the second object ID likelihood determination means  107  evenly assign the probability of being persons present in the room  301  to the second ID likelihood of each of the persons detected as OBS_CAM_ 001 , OBS_CAM_ 002 , and OBS_CAM_ 003 . That is, by the second object ID likelihood determination means  107 , the person detected as OBS_CAM_ 001  is set to have a probability of 1/3 of being a person  302 A, a probability of 1/3 of being a person  302 B, and a probability of 1/3 of being a person  302 C. With respect to OBS_CAM_ 002  and OBS_CAM_ 003 , the probability is assigned in the same manner by the second object ID likelihood determination means  107 . 
     Next, in step S 1605 , the association means  109  finds an association value based upon the person position detected by the camera  305 , the second ID likelihood of the person outputted by the second object likelihood determination means  107 , and the person&#39;s ID and person position estimated by the object position estimation means  108  shown in  FIG. 11A . The association value thus found is recorded in the association value data base  109   a  (see  FIG. 8 ). 
     Next, in step S 1606 , person detection information, detected by the tag reader  304  of the first detection unit  101   a  of the first observation device  101 , is directly outputted to the object position estimation means  108  from the first observation device  101 . This is because the person detection information contains the first ID likelihood and the first position likelihood required for the process in the object position estimation means  108 . In this case, when the person detection information has been outputted from the first observation device  101  to the object position estimation means  108 , the sequence proceeds to step  1607 . In contrast, when the person detection information is not outputted from the first observation device  101  to the object position estimation means  108 , the sequence proceeds to step S 1608 . In this example, since the tag reader  304  outputs the person detection information, the sequence proceeds to the direction of YES in step S 1606 , thereby proceeding to step S 1607 . In contrast, when no person detection information is outputted from the tag reader  304 , for example, when the person  302  enters the blind spot of the tag reader  304 , the sequence proceeds to the direction of NO in step S 1606 , thereby proceeding to step S 1608 . 
     Moreover, in step S 1609 , person detection information, detected by the tag reader  304  of the first detection unit  101   a  of the first observation device  101 , is directly outputted to the object position estimation means  108  from the first observation device  101 . This is because the person detection information contains the first ID likelihood and the first position likelihood required for the process in the object position estimation means  108 . In this case, in step S 1609 , it is determined whether or not person detection information containing the first ID likelihood and the first position likelihood is outputted from the first observation device  101  to the object position estimation means  108 . When the person detection information has been outputted from the first observation device  101  to the object position estimation means  108 , the sequence proceeds to step  1610 . In contrast, when the person detection information is not outputted from the first observation device  101  to the object position estimation means  108 , the sequence returns to step S 1602 . In this example, since the tag reader  304  outputs the person detection information, the sequence proceeds to the direction of YES in step S 1609 , thereby proceeding to step S 1610 . In contrast, when no person detection information is outputted from the tag reader  304 , for example, when the person  302  enters the blind spot of the tag reader  304 , the sequence proceeds to the direction of NO in step S 1609 , thereby returning to step S 1602 . 
     In step S 1607 , based upon the first ID likelihood and the first position likelihood of the person detected by the tag reader  304  of the first detection unit  101   a  of the first observation device  101 , the second position likelihood of the person detected by the camera  305  of the second detection unit  102   a  of the second observation device  102 , and the second ID likelihood of the person outputted by the second object ID likelihood determination means  107 , the object position estimation means  108  estimates the position of the person. Based upon this position estimation, the person&#39;s ID and the position of the person are updated (see  FIG. 11B  updated from  FIG. 11A ). In this example, since both of the tag reader  304  and the camera  305  output person detection information, the process of step S 1607  is carried out. Thereafter, the sequence returns to step S 1602 . 
     Moreover, in step S 1608 , based upon the second position likelihood of the person detected by the camera  305  of the second detection unit  102   a  of the second observation device  102  and the second ID likelihood of the person outputted by the second object ID likelihood determination means  107 , the object position estimation means  108  estimates the position of the person. Moreover, based upon this position estimation, the person&#39;s ID and the position of the person are updated (see  FIG. 11B  updated from  FIG. 11A ). Thereafter, the sequence returns to step S 1602 . 
     Moreover, in step S 1610 , based upon the first ID likelihood and the first position likelihood of the person detected by the tag reader  304  of the first detection unit  101   a  of the first observation device  101 , the object position estimation means  108  estimates the position of the person. Based upon this position estimation, the person&#39;s ID and the position of the person are updated (see  FIG. 11B  updated from  FIG. 11A ). Thereafter, the sequence returns to step S 1602 . 
     Moreover, in step S 1602  returned thereto from step S 1607 , or step S 1608 , or step S 1610 , a person  302  is detected by the tag reader  304  and the camera  305  at the next time 2008/09/02 — 12:00:01 (see  FIG. 10B  with respect to the detected state, and see  FIGS. 5 and 6  with respect to the detected positional coordinates). 
     Next, in step S 1603 , it is determined whether or not the person detection information is outputted from the second observation device  102  to the second object ID likelihood determination means  107 . When the person detection information has been outputted from the second observation device  102  to the second object ID likelihood determination means  107 , the sequence proceeds to step  1604 . In contrast, when the person detection information is not outputted from the second observation device  102  to the second object ID likelihood determination means  107 , the sequence proceeds to step S 1609 . 
     Next, in step S 1604 , the second object ID likelihood determination means  107  determines the second ID likelihood of each of the persons detected by the second observation device  102 . That is, in the same manner as in time 2008/09/02 — 12:00:00, the second ID likelihood determination means  107  needs to determine the second ID likelihood of each of the persons detected as OBS_CAM_ 004 , OBS_CAM_ 005 , and OBS_CAM_ 006 , at time 2008/09/02 — 12:00:01. In this case, however, suppose that the object tracking state determination means  106  has determined that OBS_CAM_ 004  corresponds to person detection information obtained by tracking OBS_CAM_ 001  because of sameness of the amount of color feature given by the output of the object tracking state determination means  106 . Moreover, in the same manner, suppose that the object tracking state determination means  106  has determined that OBS_CAM_ 005  corresponds to person detection information obtained by tracking OBS_CAM_ 002  and that OBS_CAM_ 006  corresponds to person detection information obtained by tracking OBS_CAM_ 003 . Then, based upon the determined information by the object tracking state determination means  106 , the second object ID likelihood determination means  107  outputs the association value of OBS_CAM_ 001  recorded in the association value data base  109   a  as the second ID likelihood of the person of OBS_CAM_ 004 . With respect to OBS_CAM_ 005  and OBS_CAM_ 006 , the second object ID likelihood determination means  107  gives outputs in the same manner. 
     In the case when an observation value OBS_CAM_ 004  is obtained by an observation at this time, a set of two observation IDs of observation ID=OBS_CAM_ 004  and observation ID=OBS_CAM_ 001  is outputted from the object tracking state determination means  106  to the second object ID likelihood determination means  107 . When the second object ID likelihood determination means  107  receives the set of observation IDs from the object tracking state determination means  106 , the second object ID likelihood determination means  107  reads the association value of observation ID=OBS_CAM_ 001  from the association value data base  109   a  so as to find the ID likelihood of observation ID=OBS_CAM_ 004 . In this case, a method is required so as to distinguish the observation ID to be used for finding the ID likelihood and the observation ID to be used for reading the association value. As this method, for example, an observation value whose association value is recorded in the association value data base  109   a  is determined as the observation value for which its association value needs to be read. 
     Next, in step S 1605 , step S 1606 , and step S 1609 , the same processes as explained earlier are carried out. 
     In step S 1607 , based upon the first ID likelihood and the first position likelihood of the person detected by the tag reader  304  of the first detection unit  101   a  of the first observation device  101 , the second position likelihood of the person detected by the camera  305  of the second detection unit  102   a  of the second observation device  102 , and the second ID likelihood outputted from the second object ID likelihood determination means  107 , the object position estimation means  108  estimates the position of the person. Moreover, based upon this position estimation, the ID of the person and the position of the person are updated (see  FIG. 11D  updated from  FIG. 11C ). Thereafter, the sequence returns to step S 1602 . 
     Moreover, in step S 1608 , based upon the second position likelihood of the person detected by the camera  305  of the second detection unit  102   a  of the second observation device  102  and the second ID likelihood of the person outputted from the second object ID likelihood determination means  107 , the object position estimation means  108  estimates the position of the person. Based upon this position estimation, the ID of the person and the position of the person are updated (see  FIG. 11D  updated from  FIG. 11C ). Thereafter, the sequence returns to step S 1602 . 
     Furthermore, in step S 1610 , based upon the first ID likelihood and the first position likelihood of the person detected by the tag reader  304  of the first detection unit  101   a  of the first observation device  101 , the object position estimation means  108  estimates the position of the person. Based upon this position estimation, the ID of the person and the position of the person are updated (see  FIG. 11D  updated from  FIG. 11C ). Thereafter, the sequence returns to step S 1602 . 
     Then, at time 2008/09/02 — 12:00:02 and thereafter, the same processes are carried out, and the ID of the person and the position of the person are updated (see  FIG. 11F  updated from  FIG. 11E ). 
     In this embodiment, an explanation has been given by exemplifying an indoor space, that is, the room  301 ; however, the present system may be utilized even in an outdoor space, as long as persons to be managed are allowed to carry tags. For example, a gate-type tag reader is attached to a school gate or a staircase located between a schoolhouse and a ground so that those students present in the schoolhouse and those present in the ground can be identified. Moreover, by attaching a tag reader and a camera to a barrier of a ground or a schoolhouse, it is possible to estimate which positions in the ground students are present. In the case when the ground has a large area to cause difficulty in observing all the ground by using a single tag reader and a single camera, the number of the tag readers and cameras may be increased. 
     With the above-mentioned structure, in the case when the second observation device  102  is successfully tracking the object, the association value obtained at the time of the previous detection of the object may replace the second ID likelihood of the object detected by the second detection unit  102   a  and the image processing unit  102   b  of the second observation device  102  at this time. Thus, the corresponding processes of the object position estimation means  108  can be carried out. 
     Second Embodiment 
     The following description will discuss the estimating state of the object position estimation means  108 . Since positions of persons shown in  FIG. 11A  are randomly set in their initial values, those positions are completely different from actual positions at which the persons are present ( FIG. 9A ).  FIG. 11B  shows the results of object position estimations carried out based upon information of the person detected by the first observation device  101  and the second observation device  102  at time 2008/09/02 — 12:00:00. Although the estimated positions are made closer to the actual positions where the persons are present ( FIG. 9A ), there are still positional errors about 2 m. This is because the positions of the persons are updated based upon the initial positions of the persons. That is, immediately after the activation of the object position estimation system, the estimation precision of the object positions is low. Accordingly, the precision of the association value of the association means  109  that utilizes the results of object position estimations of the object position estimation means  108  is also lowered. 
     Therefore, based upon the object IDs and positions of the objects detected by the first detection unit  101   a  of the first observation device  101 , the association means  109  may find the association values relating to the objects detected by the second detection unit  102   a  and the image processing unit  102   b  of the second observation device  102 . The following description will exemplify a structure in which the tag reader  304  is used as the first detection unit  101   a  of the first observation device  101 . As described above, the position detecting precision of the tag reader  304  is low. For this reason, the association means  109  may change pieces of information to be used for calculating the association value between the time before the convergence of the object position estimation results of the object position estimation means  108  and the time after the convergence thereof. More specifically, up to the convergence of the object position estimation results of the object position estimation means  108 , the association values relating to the objects detected by the second detection unit  102   a  and the image processing unit  102   b  of the second observation device  102  are found, based upon the object IDs and the positions of the objects detected by the tag reader  304 . In contrast, after the convergence of the object position estimation results of the object position estimation means  108 , the association values relating to the objects detected by the second detection unit  102   a  and the image processing unit  102   b  of the second observation device  102  may be found based upon the results of the object position estimation. 
     The following description will discuss one example as to how to determine the convergence of the object position by the association means  109 . In the case when, for example, the above-mentioned Kalman filter is used as the updating method of the object position, the estimated position of the object is outputted as a Gaussian distribution represented by average and dispersion. In this case, at the time when the association means  109  has determined that the average dispersion of all the objects becomes less than a threshold value, the association means  109  may determine that the object positions have been converged. Moreover, the converging state of the object positions (for example, information indicating that the object positions are converged after observations of N-number of times since the activation (for example, N is an integer exceeding 1)) may be preliminarily confirmed by the association means  109  by preliminary experiments, and the association means  109  is allowed to determine that the object positions are not converged up to the observations of N-number of times from the activation of the object position estimation system. In this case, the object position estimation means  108  is supposed to have a counter used for confirming how many times the object positions are updated. 
     By using the above-mentioned arrangement, immediately after the activation of the object position estimation system of the present invention, the association means  109  can find the association value by using output information of the first observation device  101 . With this arrangement, it becomes possible to calculate the association value more accurately, that is, the second ID likelihood more accurately. 
     Additionally, in the respective embodiments, the respective portions of the first detection unit  101   a  functioning as the first observation unit, the first object position likelihood determination means  103 , the first object ID likelihood determination means  104 , the second detection unit  102   a  functioning as the second observation unit, the second object position likelihood determination means  105 , the object tracking state determination means  106 , the second object ID likelihood determination means  107 , the association means  109 , and the object position estimation means  108 , or desired portions thereof may be configured by pieces of software. Thus, a computer program having steps forming controlling operations of the respective embodiments of the specification of the present invention is prepared so that the program is readably stored in a recording medium, such as a storage device (hard disk or the like), and by allowing a computer to read the computer program and store it in a temporary storage device (semiconductor memory, or the like) and to execute by using the CPU, the respective functions or steps can be executed. 
     By properly combining the arbitrary embodiments of the aforementioned various embodiments, the effects possessed by the embodiments can be produced. 
     INDUSTRIAL APPLICABILITY 
     The object position estimation system, object position estimation device, object position estimation method, and object position estimation program in accordance with the present invention make it possible to estimate positions of objects even when an observation device having no ID identifying function for the objects is included. Monitoring camera systems or the like have been widely introduced; however, in most of these systems, images in the monitoring region are stored and identifying processes of the subject images are generally carried out by manually viewing processes. Since the present invention makes it possible to identify persons and their positions without the need of manual operations, the positions and moving lines of persons can be automatically obtained and managed so that an effective managing system for persons, which has features that have not achieved conventionally, can be achieved and effectively applied for security purpose. Moreover, the present invention is also applicable to a system or the like that manages positions of articles, such as containers, in the physical distribution field of the like. 
     Although the present invention has been fully described in connection with the preferred embodiments thereof with reference to the accompanying drawings, it is to be noted that various changes and modifications are apparent to those skilled in the art. Such changes and modifications are to be understood as included within the scope of the present invention as defined by the appended claims unless they depart therefrom.