Patent Publication Number: US-11386536-B2

Title: Image analysis device, image analysis method and image analysis program

Description:
The present application is a Continuation application of Ser. No. 16/476,316 filed on Jul. 8, 2019, which is a National Stage Entry of PCT/JP2017/012956 filed on Mar. 29, 2017, the contents of all of which are incorporated herein by reference, in their entirety. 
    
    
     TECHNICAL FIELD 
     The present invention relates to an image analysis device, an image analysis method, and an image analysis program. 
     BACKGROUND ART 
     Patent Literature 1 describes one example of an image analysis system of a related art. As shown in  FIG. 15 , the image analysis system of Patent Literature 1 includes a camera  12  for photographing an object, a picture acquiring part  20 , a picture correcting part  22 , a position detection part  24 , and a reliability operation part  26 . 
     The image analysis system of Patent Literature 1 having such a configuration operates as follows. 
     Specifically, the picture acquiring part  20  takes a picture of a pointing marker as the object. The picture correcting part  22  corrects the picture so as to easily detect the marker. The position detection part  24  estimates a position of the marker in the picture. The reliability operation part  26  calculates a reliability of the position and selects only data having a high reliability. 
     Patent Literature 2 discloses a “picture recognition system” which adaptively learns and renews recognition units in conformity with various environments and objects so as to enable highly-accurate and robust recognition even in a situation where the recognition units are limited. 
     The picture recognition system described in Patent Literature 2 basically includes a recognition processing part for processing input picture data in parallel using a plurality of recognition units, an integration part for integrating outputs of the respective recognition units, and a learning part for learnedly renewing the recognition units using teaching data as a target of processing. 
     The recognition processing part processes the input pictures in parallel with the plurality of recognition units, extracts an object, and produces a processed picture. As the recognition unit, a tree structure picture filter is used which includes a plurality of picture filters combined in a tree structure. In order to deal with various scenes, optimization of the tree structure is previously executed in off-line prior learning and is stocked in a recognition unit database as recognition units specialized for typical scenes, for example, daytime, night, weather, and an environment (an expressway, a main road, an urban area, or the like). 
     The learning part includes a recognition unit evaluation part for evaluating the individual recognition units, a switching selection part for calculating an optimal combination from all of the recognition units and for switching a combination of currently used recognition units into the optimal combination, and a sequential learning part for creating new recognition units on the basis of the teaching data. 
     The recognition unit evaluation part individually evaluates, in response to input of the picture serving as the teaching data, a train of tree structure filters currently used and a train of tree structure filters stocked in the recognition unit database. 
     The switching selection part calculates, among all of trees including currently used trees and stocked trees, a combination of N trees with the highest evaluation. 
     As regards an optimal combination of trees, evaluation is carried out using an integrated picture of a group of combined trees. For instance, an evaluated value is calculated by comparing the integrated picture with a target picture. 
     Patent Literature 3 discloses a “picture recognition method and apparatus” for carrying out appropriate picture recognition by automatically processing picture data with a proper algorithm even if there are a lot of kinds of objects and picture data of the objects are varied in characteristics. 
     The picture recognition apparatus disclosed in Patent Literature 3 includes an imaging camera and a picture processing device. The picture processing device includes an A/D converter, a picture data memory, a central processing unit, a program memory, and first through sixth picture processing modules. Each picture processing module includes a memory and a processing unit. 
     An object is photographed with the imaging camera. An image signal produced by the imaging camera is supplied to the picture processing device. The supplied image signal is converted into an 8-bit digital signal by the A/D converter and is stored in the picture data memory. The central processing unit sends, in accordance with a program stored in the program memory, the picture data and programs of processing contents (algorithms) to the respective picture processing modules, respectively. 
     The picture data and the programs of processing contents, which are sent to the respective picture processing modules, are stored in the memories. The processing units process the stored picture data in accordance with the programs of processing contents and inform the central processing unit of results. The respective picture processing modules have different threshold values for binarization. 
     The central processing unit checks the results of picture processing sent from the respective picture processing modules, selects a picture processing module which produces an optimal solution, and computes a shape and a position (including a posture) of the object by using results thereof to obtain a recognized result. 
     CITATION LIST 
     Patent Literatures 
     PL1: JP 2009-245349 A 
     PL2: JP 2008-204103 A 
     PL3: JP 2000-194861 A 
     Non Patent Literature 
     NPL 1: Said Pertuz, Domenec Puig, Miguel Angel Garcia, “Analysis of focus measure operators for shape-from-focus”, Pattern Recognition, November 2012 
     SUMMARY OF INVENTION 
     Technical Problem 
     Each of Patent Literatures 1-3 mentioned above has a problem which will be described hereinunder. 
     Patent Literature 1 has a problem that applicable environmental conditions are limited. This is because, in a prior art, picture correction is carried out supposing an environment where a certain restriction is imposed on an installation environment of illumination and a camera, such as an inside of a room of an ordinary home. However, such picture correction is difficult to apply to a use, such as street surveillance, where various environmental variations exist, for example, influence of the installation position or sunshine, strong wind, rainfall, and so on. 
     Patent Literature 2 merely discloses a technical idea of adaptively learning and renewing the recognition units in conformity with various environments or objects. That is, Patent Literature 2 never sorts out optimal picture data among supplied picture data. 
     Patent Literature 3 checks the results of picture processing after it is executed in all of the picture processing modules, and selects the picture processing module producing the optimal solution. Consequently, Patent Literature 3 has a problem that consumption of unnecessary calculation resources occurs. 
     Object of Invention 
     It is an object of this invention to provide an image analysis device which is capable of resolving any of the above-mentioned problems. 
     Solution to Problem 
     An image analysis device of this invention comprises a plurality of process execution units configured to be capable of executing different processes on an image inputted; an analysis unit configured to analyze, on the basis of the image, a variation of the image caused by an external environment; and a process selection unit configured to select, on the basis of the analyzed variation, at least one from among the plurality of process execution units. 
     Advantageous Effects of Invention 
     As an advantageous effect of this invention, it is possible to automatically deal with various environmental variations without consuming unnecessary calculation resources. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram for illustrating a configuration of an image analysis device according to an example embodiment of this invention; 
         FIG. 2  is a flow chart for illustrating an operation of the image analysis device illustrated in  FIG. 1 ; 
         FIG. 3  is a block diagram for illustrating hardware configuration of the image analysis device illustrated in  FIG. 1 ; 
         FIG. 4  is a block diagram for illustrating a configuration of an image analysis device according to a first example of this invention; 
         FIG. 5  is a view for explaining an example of sorting characteristic information stored in a characteristic memorizing unit for use in the image analysis device illustrated in  FIG. 4 ; 
         FIG. 6  is a view for explaining an example of an environment information-sorting function correspondence rule stored in the characteristic memorizing unit for use in the image analysis device illustrated in  FIG. 4 ; 
         FIG. 7  is a flow chart for illustrating a sorting selection operation of the image analysis device illustrated in  FIG. 4 ; 
         FIG. 8  is a block diagram of a face authentication device to which the image analysis device illustrated in  FIG. 1  is supposed to be applied; 
         FIG. 9  is a concept view of environment information for illustrating an operation of a sorting switching unit for use in the image analysis device illustrated in  FIG. 4 ; 
         FIG. 10  is a block diagram for illustrating a configuration of an image analysis device according to a second example of this invention; 
         FIG. 11  is a flow chart for illustrating a sorting selection operation of the image analysis device illustrated in  FIG. 10 ; 
         FIG. 12  is a flow chart for illustrating a sorting operation of the image analysis device illustrated in  FIG. 10 ; 
         FIG. 13  is a view for explaining an example of integration processing for plural sorting processing by a result integration unit of the image analysis device illustrated in  FIG. 10 ; 
         FIG. 14  is a block diagram for illustrating a configuration of an intelligent camera according to a third example of this invention; and 
         FIG. 15  is a block diagram for illustrating a configuration of an image analysis system disclosed in Patent Literature 1. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Now, description will be made in detail about example embodiments of this invention with reference to the drawings. 
       FIG. 1  is a block diagram for illustrating a configuration of an image analysis device  100  according to an example embodiment of this invention 
     The image analysis device  100  illustrated in the figure includes first through N-th process execution units  110 - 1 ,  110 - 2 , . . . , and  110 -N, where N represents an integer which is not less than two, an analysis unit  120 , and a process selection unit  130 . 
     Each of the first through the N-th process execution units  110 - 1  to  110 -N is capable of executing a different process on an input image. The analysis unit  120  analyzes, on the basis of the image, a variation (including degradation) in the image caused by an external environment. The process selection unit  130  selects, on the basis of the analyzed variation, at least one from among the first through the N-th process execution units  110 - 1  to  110 -N and makes the selected process execution unit execute a process on the image. 
     As described above, in the example embodiment, all of the first through the N-th process execution units  110 - 1  to  110 -N do not execute the process on the image but a process execution unit to be used is selected before the execution. With this structure, consumption of unnecessary calculation resources is suppressed. 
     Now, referring to  FIG. 2 , an operation of the image analysis device  100  will be described. 
     First, the analysis unit  120  analyzes, on the basis of an input image, a variation (including degradation) in the image caused by an external environment (Step S 101 ). 
     Next, the process selection unit  130  selects, on the basis of the analyzed variation, at least one from among the first through the N-th process execution units  110 - 1  to  110 -N (Step S 102 ). 
     The respective units (components) of the image analysis device  100  may be implemented by the use of a combination of hardware and software. 
       FIG. 3  is a block diagram for illustrating an image analysis device  200  representing a mode in which the image analysis device  100  is implemented by combining hardware with software. 
     The image analysis device  200  illustrated in the figure includes an A/D converter  210 , a CPU (central processing unit)  220 , a work memory  230 , and a program memory  240 . 
     The A/D converter  210  converts an analog image signal imaged by a camera into a digital image signal. The CPU  220  serves as a control unit configured to control an operation of the image analysis device  200 . The work memory  230  is, for example, constituted by an RAM (random access memory) and temporarily stores the digital image signal and processed results by the CPU  220 . The program memory  240  is, for example, constituted by an ROM (read only memory) or the RAM and stores an image analysis program. 
     The CPU  220  executes, on the basis of the image analysis program stored in the program memory  240 , predetermined processing to thereby implement the respective units (components) as various kinds of means. Furthermore, the image analysis program may be recorded in a recording medium to be distributed. The image analysis program recorded in the recording medium is read into a memory via a wire, wirelessly, or via the recording medium itself to operate the control unit and so on. Examples of the recording medium include an optical disc, a magnetic disk, a semiconductor memory device, and a hard disk. 
     When the above-mentioned example embodiment is described in another expression, the example embodiment can be implemented by causing a computer, which is to be operated as the image analysis device  200 , to operate as the first through the N-th process execution units  110 - 1  to  110 -N, the analysis unit  120 , and the process selection unit  130  based on the image analysis program stored in the program memory  240 . 
     In the manner as described above, according to the example embodiment of the present invention, it is possible to automatically deal with a wide variety of environmental variations without consuming unnecessary calculation resources. 
     Example 1 
     [Description of Configuration] 
     New, referring to the drawings, a first example of this invention will be described. 
       FIG. 4  is a block diagram for illustrating a configuration of an image analysis device  300  according to a first example of this invention. 
     Referring to  FIG. 4 , the image analysis device  300  according to the first example of this invention includes a data input unit  310 , an analyzing unit  320 , a sorting switching unit  330 , an environment analysis unit  340 , a sorting selection unit  350 , a characteristic storage unit  360 , a plurality of sorting units  370 - 1 ,  370 - 2 , . . . , and a data output unit  380 . 
     Roughly explaining, each of the above-mentioned means operates as follows. 
     The data input unit  310  acquires process target data from a data acquiring device, for example, a camera. The analyzing unit  320  receives the data acquired by the data input unit  310  and carries out analyzing processing such as detection of an object in an image, detection of an abnormal sound in a voice, and so on. When data for additional analysis is specified from the sorting selection unit  350 , the analyzing unit  320  carries out the additional analysis to be added to a result. 
     The sorting switching unit  330  switches delivery of data so as to deliver the result of the analyzing unit  320  to a sorting unit  370  specified by the sorting selection unit  350 . 
     The environment analysis unit  340  receives the data acquired by the data input unit  310  and analyzes environment information such as brightness and blurring in the image. It is noted here that an analysis of the environment information need not be carried out for all of input data and may be carried out at any desired execution frequency in accordance with contents of the environment analysis, for example, by periodic sampling. The sorting selection unit  350  selects an optimal sorting unit  370  in accordance with the environment information analyzed by the environment analysis unit  340 , and sorting characteristic information and an environment information-sorting function correspondence rule which are stored in the characteristic storage unit  360 . When any additional analysis is required in the selected sorting, the sorting selection unit  350  requests the analyzing unit  320  to carry out the additional analysis. 
     Accordingly, the plurality of sorting units  370  serve as the process execution units  110 - 1  to  110 -N in  FIG. 1 . A combination of the environment analysis unit  340  and the characteristic storage unit  360  serves as the analysis unit  120  in  FIG. 1 . Furthermore, the sorting selection unit  350  serves as the process selection unit  130  in  FIG. 1   
       FIG. 5  is a view for illustrating an example of the sorting characteristic information stored in the characteristic storage unit  360 .  FIG. 6  is a view for illustrating an example of the environment information-sorting function correspondence rule stored in the characteristic storage unit  360 . 
     As shown in  FIG. 5 , the characteristic storage unit  360  memorizes, as the sorting characteristic information, sorting function information for specifying the sorting function, additional analysis function information indicative of the additional analysis function to be requested to the analyzing unit  360 , meta data indicative of data used in the sorting, and a transition time interval indicative of a time interval required for switching of the sorting function. It is noted here that, as the sorting function, not only a string of characters indicative of the function but also a numerical value or a flag may be used. As the additional analysis function, not only a string of characters indicative of the function but also a numerical value or a flag may be used. As the meta data, not only a string of characters indicative of a data type but also a numerical value or a flag may be used. 
     As shown in  FIG. 6 , the characteristic storage unit  360  memorizes, as the environment information-sorting function correspondence rule, the sorting function information for specifying the sorting function and rule information indicative of the rule for selecting the sorting. It is noted here that, as the sorting function, not only a string of characters indicative of the function but also a numerical value or a flag may be used. As the rule, not only a simple threshold expression but also a selection algorithm by a decision tree or machine learning, and so on may be used. 
     Each of the sorting units  370 - 1 ,  370 - 2 , . . . decides, on the basis of the analyzed result produced by the analyzing unit  320  and the meta data thereof, whether the analyzed result is produced or discarded. The data output unit  380  outputs the received sorted data to an external system. 
     [Description of Operation] 
     Next referring to  FIG. 4  and a flow chart of  FIG. 7 , description will be made in detail about a whole operation of the image analysis device  300  according to the first example. 
     First of all, the data input unit  310  acquires object data from the camera or the like (Step S 201  in  FIG. 7 ). 
     Subsequently, the analyzing unit  320  analyzes the object data to obtain analyzed result data (Step S 202  in  FIG. 7 ). In this event, if the additional analysis is designated by the sorting selection unit  350 , the analyzing unit  320  carries out the additional analysis together (Step S 201  in  FIG. 7 ). 
     At the same time, the environment analysis unit  340  analyzes the object data to obtain the environment information (Step S 203  in  FIG. 7 ). The sorting selection unit  350  selects the sorting unit  370  in accordance with the environment information analyzed by the environment analysis unit  340 , and the sorting characteristic information and the environment information-sorting function correspondence rule which are stored in the characteristic storage unit  360  (Step S 204  in  FIG. 7 ). 
     The sorting switching unit  330  delivers the analyzed result data produced by the analyzing unit  320  to the sorting unit  370  decided by the sorting selection unit  350  (Step S 205  in  FIG. 7 ). The sorting unit  370  carries out the sorting processing on the received analyzed result data and sorts out the analyzed result data required for maintenance of accuracy (Step S 206  in  FIG. 7 ). The data output unit  380  transmits, to the external system, the sorted analyzed result data received (Step S 207  in  FIG. 7 ). Until the system is stopped, the above-mentioned steps S 201  to S 207  are repeated (Step S 208  in  FIG. 8 ). 
     Although the step S 202  and the steps S 203  and S 204  operate in parallel in the above-mentioned operation example, those steps may operate in series. 
     [Description of Operation Example] 
     Next, an example of the sorting selection processing by the sorting selection unit  350  will be described. It is assumed here that face authentication from a surveillance image is carried out and that a system includes detection of a face from the image, extraction of features from the face, and DB (database) collation is used. Furthermore, it is assumed that the detection of the face is achieved by using the first example. 
       FIG. 8  is a block diagram for illustrating a configuration of a face authentication device  400  assumed in the foregoing. The face authentication device  400  includes a processing unit  420  and a storage unit  440 . The processing unit  420  includes a face detection unit  422 , a feature extraction unit  424 , and a collation unit  426 . 
     The storage unit  440  includes a database  442  for registering reference features each of which indicates a feature of a face in a person to be authenticated. 
     The face detection unit  422  detects the face from the image taken by the camera. The image analysis device  300  illustrated in  FIG. 4  is used as the face detection unit  422 . The feature extraction unit  424  extracts the feature of the detected face. The collation unit  426  collates the extracted feature with reference features registered in the database  442  to carry out the face authentication. 
     In the manner as described above, the image analysis device  300  according to the first example assumes the detection of the face by the face detection unit  422 . In the face detection unit  422 , for the surveillance image acquired by the data input unit  310 , the analyzing unit  320  carries out detection processing of the face. Herein, it is assumed that the plurality of sorting units  370  include first through third sorting units  370 - 1 ,  370 - 2 , and  370 - 3 . The first sorting unit  370 - 1  uses a simple selection filter. The second sorting unit  370 - 2  uses a single output filter relating to brightness. The third sorting unit  370 - 3  uses a plural output filter relating to blurring. 
     The simple selection filter is a simple selection filter configured to output the face having high quality which is not less than a predetermined threshold value. The single output filter is a single output filter configured to select only one face having the highest quality value among the faces of the same person detected during a past given time interval. The plural output filter is a plural output filter configured to select a plurality of faces each having a high quality value among the faces of the same person detected during the past given time interval. 
     Referring to  FIGS. 5, 6, and 9 , description will proceed to processing of the sorter selection unit  350  selecting, in a case of the three sorting units  370 - 1 ,  370 - 2 , and  370 - 3 , the optimal sorting unit  370  in accordance with the environment information. Herein, description will proceed assuming that, as an initial state, the first sorting unit (simple selection filter)  370 - 1  operates as the sorting unit  370 . 
     First, the data input unit  310  divides the image acquired from the camera into frame pictures which are sent to the analyzing unit  320  and the environment analysis unit  340 . The analyzing unit  320  carries out the face detection on the received pictures to produce a plurality of detected faces as the analyzed result data together with quality scores for the respective faces. 
     On the other hand, the environment analysis unit  340  analyzes the frame pictures produced by the data input unit  310  to produce the environment information. For example, the environment analysis unit  340  analyzes information of a blurring amount of the image and brightness bias, as shown in  FIG. 9 , per each frame picture or periodically to produce the analyzed information as the environment information. 
       FIG. 9  shows a time-based graph where the abscissa represents a time in seconds and the ordinate represents the blurring amount and a magnitude of the brightness bias, and an example of states of the analyzing unit  320  and the sorting unit  370  at that time.  FIG. 9  shows the blurring amount and the magnitude of the brightness bias each of which is normalized so that the maximum value is equal to 1.0. 
     The blurring amount may be estimated, for example, by Fourier transforming the input picture and deciding the blurring if a ratio of low frequency components is greater. Such a method of estimating the blurring amount is reported by the Non-Patent Literature 1 mentioned above. On the other hand, the brightness bias may be estimated, for example, by calculating a brightness histogram in the picture and detecting the brightness bias in a case where there are a greater amount of low-brightness pixels or a greater amount of high-brightness pixels. 
     Next, the sorting selection unit  350  selects the optimal sorting unit  370  on the basis of the environment information analyzed by the environment analysis unit  340 , and the sorting characteristic information ( FIG. 5 ) and the environment information-sorting function correspondence rule ( FIG. 6 ) which are stored in the characteristic storage unit  360 . 
     For instance, in the example of the environment information illustrated in  FIG. 9 , both of brightness and the blurring amount are low from a time instant  1  to a time instant  13 . Therefore, the sorting selection unit  350  selects the simple selection filter because rules for both of the single output filter (brightness) and the plural output filter (blurring) are not satisfied in the environment information-sorting function correspondence rule in  FIG. 6 . The sorting switching unit  330  delivers, in accordance with selection by the sorting selection unit  350 , the analyzed result data produced by the analyzing unit  320  to the first sorting unit  370 - 1  using the simple selection filter. The first sorting unit  370 - 1  using the simple selection filter compares a quality score of each face included in the supplied analyzed result data with the predetermined threshold value and sends the analyzed result data to the data output unit  380  if the quality score is not less than the threshold value. As a result, in a case where the influence on accuracy due to the environment is low, only a high-quality face using a high threshold value is selected. Accordingly, in the feature extraction ( 424  in  FIG. 8 ) from the faces and the DB collation ( 426  in  FIG. 8 ) in a latter part, the face authentication with high recall and precision can be achieved using the face picture having the high quality. 
     When the blurring amount is as large as 0.9 at a time instant  14 , the sorting selection unit  350  selects the third sorting unit  370 - 3  using the plural output filter because a rule for the plural output filter (blurring) is satisfied in the environment information-sorting function correspondence rule in  FIG. 6 . The analyzing unit  320  carries out, together with the above-mentioned face detection, a tracking analysis for extracting tracking information of the same face among the frame pictures specified by the sorting selection unit  350 . The sorting switching unit  330  delivers, in accordance with selection of the sorting selection unit  350 , the analyzed result data produced by the analyzing unit  320  to the third sorting unit  370 - 3  using the plural output filter. The third sorting unit  370 - 3  using the plural output filter maintains the same face during the past given time interval using a tracked result included in the supplied analyzed result data, and delivers, to the data output unit  380 , one having the highest quality score thereamong. As a result, even in a photographing condition where the image is blurred under the influence of wind and so on and the face having the high quality is not picked up, a plurality of faces each having a good quality are selected thereamong as candidates. Accordingly, in the feature extraction from the faces ( 424  in  FIG. 8 ) and the DB collation ( 426  in  FIG. 8 ) in a latter part, it is possible to achieve the face authentication with the recall maintained even if the environment has unfavorable conditions. 
     [Description of Effect] 
     Next, an effect of the first example embodiment will be described. 
     According to the first example, the sorting selection unit  350  selects the optimal sorting unit  370  on the basis of the environment information analyzed by the environment analysis unit  340 . Therefore, it is possible to provide the analyzed result data required to maintain the accuracy in recall and so on even in a case where various environmental variations such as influences of an installation position and sunshine, strong wind, rainfall, and so on exist as in the street surveillance. 
     Respective parts (respective components) of the image analysis device  300  may be implemented by using a combination of hardware and software, as shown in  FIG. 3 . In a form in which the hardware and the software are combined, the respective parts (the respective components) are implemented as various kinds of means by storing an image analysis program in the program memory  240  and making the hardware of the CPU  220  operate based on the image analysis program. The image analysis program may be recorded in a recording medium to be distributed. The program recorded in the recording medium is read into a memory via a wire, wirelessly, or via the recording medium itself to operate the control unit and so on. By way of example, the recording medium may be an optical disc, a magnetic disk, a semiconductor memory device, a hard disk, or the like. 
     Explaining the above-mentioned first example in another expression, the first example can be implemented by making a computer to be operated as the image analysis device  300  act as the data input unit  310 , the analyzing unit  320 , the sorting switching unit  330 , the environment analysis unit  340 , the sorting selection unit  350 , the characteristic storage unit  360 , the plurality of sorting units  370 , and the data output unit  380  according to the image analysis program stored in the program memory  240 . 
     Example 2 
     [Description of Configuration] 
     New, referring to the accompanying drawings, a second example of this invention will be described in detail. 
       FIG. 10  is a block diagram for illustrating a configuration of an image analysis device  300 A according to the second example of this invention. 
     Referring to  FIG. 10 , the image analysis device  300 A according to the second example of this invention includes, in addition to the configuration of the first example, a result integration unit  390  connected between the sorting units  370  and the data output unit  380 . 
     [Description of Operation] 
     Roughly explaining, those means operate as follows, respectively. 
     The sorting selection unit  350  selects a plurality of sorting units  370  in accordance with the environment information analyzed by the environment analysis unit  340 , sorting characteristic information and the environment information-sorting function correspondence rule which are stored in the characteristic storage unit  360 . 
     The result integration unit  390  integrates, in accordance with results selected by the plurality of sorting units  370  and weighting specified by the sorting selection unit  350 , analyzed result data to be finally produced and sends integrated analyzed result data to the data output unit  380 . 
     Next referring to flow charts in  FIG. 11  and  FIG. 12 , description will be made in detail about a whole operation of the image analysis device  300 A according to the second example. 
     First, the sorting selection unit  350  selects one or more sorting units  370  in accordance with the environment information produced by the environment analysis unit  340 , and the sorting characteristic information and the environment information-sorting function correspondence rule which are stored in the characteristic storage unit  360  (Step S 204 A in  FIG. 11 ). 
       FIG. 12  is a flow chart for use in explaining an operation of selection execution (Step S 300 ) in the sorting selection unit  350  in  FIG. 11 . 
     Next, when only one sorting unit  370  is selected (No in Step S 301 ), the single sorting unit sorts the analyzed result data in the manner similar to the above-mentioned first example (Steps S 301 , S 305 , and S 306  in  FIG. 12 ). 
     On the other hand, when a plurality of sorting units  370  are selected (Yes in Step S 301 ), the sorting switching unit  330  delivers the analyzed result data to the plurality of sorting units  370  (Step S 302  in  FIG. 12 ). Receiving the data, each sorting unit  370  carries out sorting processing and delivers a result to the result integration unit  390  (Step S 303  in  FIG. 12 ). The result integration unit  390  integrates, in accordance with results selected by the plurality of sorting units  370  and the weighting specified by the sorting selection unit  350 , the analyzed result data to be finally produced and sends the integrated analyzed result data to the data output unit  380  (Step S 304  in  FIG. 12 ). 
     [Description of Operation Example] 
     Next, an example of integration processing for plural sorting processing by the result integration unit  390  will be described. Herein, with reference to  FIGS. 10 and 13 , face detection processing will be described as regards a case of executing the plurality of sorting units  370  in consideration of a transition time interval until the sorting units  370  produce stable results. Herein, description will be made assuming that, as an initial state, the first sorting unit  370 - 1  using the simple selection filter operates as the sorting unit  370 . 
     First, the sorting selection unit  350  selects the sorting units on the basis of the environment information analyzed by the environment analysis unit  340  and the environment information-sorting function correspondence rule stored in the characteristic storage unit  360 . 
     The blurring amount is as large as 0.9 at a time instant  14  in  FIG. 13 . Therefore, the sorting selection unit  350  selects the third sorting unit  370 - 3  using the plural output filter because a rule for the plural output filter (blurring) is satisfied in the environment information-sorting function correspondence rule in  FIG. 6 . 
     Based on the transition time interval of the sorting characteristic information in  FIG. 5 , the sorting selection unit  350  decides to execute the simple selection filter also during 2,000 milliseconds until the plural output filter (blurring) is stabilized, and instructs the analyzing unit  320  to start tracking analysis and the result integration unit  390  to prioritize the result of the first sorting unit  370 - 1  using the simple selection filter. 
     The result integration unit  390  produces, in accordance with the instruction of the sorting selection unit  350 , only the result of the first sorting unit  370 - 1  using the simple selection filter among outputs of the two sorting units  370 . 
     At a time instant  16  in  FIG. 13 , the sorting selection unit  350  determines an end of the transition period and stops the use of the first sorting unit  370 - 1  using the simple selection filter. As a result, the result integration unit  390  produces the result of the one sorting unit  370  (in this example, the third sorting unit  370 - 3  using the plural output filter) as it is to a latter part. Accordingly, it is possible to use also a sorting algorithm in which an operation immediately after start of the execution is not stabilized. 
     [Description of Effect] 
     Next, an effect of the second example embodiment will be described. 
     According to the second example, the sorting selection unit  350  selects one or more sorting units  370  and the result integration unit  390  integrates the results of the plurality of sorting units  370 . Therefore, it is possible to achieve higher accuracy by the sorting algorithm requiring a long initialization time interval and integration of a plurality of kinds of sorting. 
     Respective parts (respective components) of the image analysis device  300 A may be implemented by using a combination of hardware and software, as shown in  FIG. 3 . In a form in which the hardware and the software are combined, the respective parts (the respective components) are implemented as various kinds of means by storing an image analysis program in the program memory  240  and making the hardware of the CPU  220  operate based on the image analysis program. The image analysis program may be recorded in a recording medium to be distributed. The program recorded in the recording medium is read into a memory via a wire, wirelessly, or via the recording medium itself to operate the control unit and so on. By way of example, the recording medium may be an optical disc, a magnetic disk, a semiconductor memory device, a hard disk, or the like. 
     Explaining the above-mentioned second example in another expression, the second example can be implemented by making a computer to be operated as the image analysis device  300 A act as the data input unit  310 , the analyzing unit  320 , the sorting switching unit  330 , the environment analysis unit  340 , the sorting selection unit  350 , the characteristic storage unit  360 , the plurality of sorting units  370 , the data output unit  380 , and the result integration unit  390  according to the image analysis program stored in the program memory  240 . 
     Example 3 
     Now, a third example of this invention will be described. The third example is an example in which the above-mentioned image analysis devices  100 ,  300 , and  300 A are applied to an intelligent camera  500 . 
       FIG. 14  is a block diagram for illustrating a configuration of the intelligent camera  500  according to the third example. The intelligent camera  500  illustrated in the figure incorporates an acquiring unit  510  and a picture processing unit  520 . The acquiring unit  510  acquires an image. The picture processing unit  520  serves as any of the above-mentioned image analysis devices  100 ,  300 , and  300 A. 
     This invention is not limited to the configurations of the above-mentioned example embodiments (the examples), and this invention involves any changes in a range not departing from the gist of this invention. 
     While this invention has been described with reference to the example embodiments and examples thereof, this invention is not limited to the foregoing example embodiments and examples. The configurations and the details of this invention may be modified within the scope of this invention in various manners which could be understood by those of ordinary skill. 
     INDUSTRIAL APPLICABILITY 
     This invention is applicable to uses such as an analysis system for analyzing the image or the picture data with high accuracy or a program for achieving the analysis system by a computer. In addition, this invention is also applicable to an analysis device in a surveillance system such as street surveillance in the streets or a program for achieving the analysis device by a computer. 
     REFERENCE SIGNS LIST 
       100  image analysis device 
       110 - 1 ,  110 - 2 ,  110 -N process execution unit 
       120  analysis unit 
       130  process selection unit 
       200  image analysis device 
       210  A/D converter 
       220  CPU 
       230  work memory 
       240  program memory 
       300 ,  300 A image analysis device 
       310  data input unit 
       320  analyzing unit 
       330  sorting switching unit 
       340  environment analysis unit 
       350  sorting selection unit 
       360  characteristic storage unit 
       370 - 1 ,  370 - 2  sorting unit 
       380  data output unit 
       390  result integration unit 
       400  face authentication device 
       420  processing unit 
       422  face detection unit 
       424  feature extraction unit 
       426  collation unit 
       440  storage unit 
       442  database 
       500  intelligent camera 
       510  acquiring unit 
       520  picture processing unit