Abstract:
A system and method to perform multisensory data fusion in a distributed sensor environment for object identification classification. Embodiments of the invention are sensor-agnostic and capable handling a large number of sensors of different types via a gateway which transmits sensor measurements to a fusion engine according to predefined rules. A relation exploiter allows combining sensor measurements with information on object relationships from a knowledge base. Also included in the knowledge base is a travel model for objects, along with a graph generator to enable forecasting of object locations for further correlation of sensor data in object identification. Multiple task managers allow multiple fusion tasks to be performed in parallel for flexibility and scalability of the system.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit of Singapore (SG) Application Number 10201403292W filed on Jun. 16, 2014 which is hereby incorporated by reference in their entirety. 
       BACKGROUND 
       [0002]    Complex data collected by sensors, such as images captured by cameras, is often difficult to interpret, on account of noise and other uncertainties. A non-limiting example of complex data interpretation is identifying a person in a public space by means of cameras or biometric sensors. Other types of sensing used in such a capacity include face recognition, microphones, and license plate readers (LPR). 
         [0003]    Existing approaches for identification systems typically perform identification based solely on a single sensor or on a set of sensors deployed at the same location. In various practical situations, this results in a loss of identification accuracy, 
         [0004]    Techniques for data fusion are well-known, in particular utilizing Bayesian methodologies, but these are typically tailored for specific sensor types or data fusion applications, often focusing on approximation methods for evaluating the Bayesian fusion formulas. When a large number of sensors is used, scalability is an important requirement from a practical perspective. In addition, when different types of sensors are used, the system should not be limited to a particular sensor type. 
         [0005]    It would be desirable to have a reliable means of reducing the uncertainties and improving the accuracy of interpreting sensor data, particularly for large numbers of sensors of mixed types. This goal is met by embodiments of the present invention. 
       SUMMARY 
       [0006]    Embodiments of the present invention provide a system to perform multisensory data fusion for identifying an object of interest in a distributed sensor environment and for classifying the object of interest. By accumulating the identification results from individual sensors an increase in identification accuracy is obtained. 
         [0007]    Embodiments of the present invention are sensor-agnostic and are capable handling a large number of sensors of different types. 
         [0008]    Exploiting additional information besides sensor measurements is uncommon. While the usage of road networks and motion models exists (see e.g. [2]), additionally exploiting relations between different object is not part of state-of-the-art. 
         [0009]    According to various embodiments of the present invention, instead of interpreting data obtained from similar sensors individually or separately, data from multiple sensors is fused together. This involves fusing data from multiple sensors of the same type (e.g., fusing only LPR data), but also fusing data from multiple sensors of different types (e.g., fusing LPR data with face recognition data). Embodiments of the invention provide for scaling systems across different magnitudes of sensor numbers. 
         [0010]    Embodiments of the present invention can be used in a wide spectrum of object identification systems, including, but not limited to: identification of cars in a city via license plate readers; and personal identification via biometric sensors and cameras. Embodiments of the invention are especially well-suited in situations where identification accuracy of surveillance systems is relatively low, such as with personal identification via face recognition in public areas. 
         [0011]    An embodiment of the invention can be employed in conjunction with an impact/threat assessment engine, to forecast a potential threat level of an object, potential next routes of the object, etc., based on the identification of the object as determined by the embodiment of the invention. In a related embodiment, early alerts, and warnings are raised when the potential threat level exceeds a predetermined threshold, allowing appropriate counter measures to be prepared. 
         [0012]    General areas of application for embodiments of the invention include, but are not limited to fields such as water management and urban security. 
         [0013]    Therefore, according to an embodiment of the present invention there is provided a data fusion system for identifying an object of interest, the data from multiple data sources, the system including: (a) a gateway, for receiving one or more sensor measurements from a sensor set; (b) a knowledge base stored in a non-transitory data storage, the knowledge base for storing information about objects of interest; (c) a relation exploiter, for extracting one or more objects from the knowledge base related to the object of interest; (d) a fusion engine, for receiving the one or more sensor measurements from the gateway, the fusion engine comprising: (e) an orchestrator module, for receiving the one or more objects from the relation exploiter related to the object of interest and for combining the one or more sensor measurements therewith; (f) at least one task manager, for receiving a fusion task from the orchestrator module, for creating a fusion task data structure therefrom, and for managing the fusion task data structure to identify the object of interest; and (g) a Bayesian fusion unit for performing the fusion task for the at least one task manager. 
         [0014]    According to another embodiment of the present invention there is provided a data fusion system for identifying an object of interest, the data from multiple data sources, the system comprising:
       a gateway, for receiving sensor measurements from a sensors set;   a knowledge base stored in a non-transitory data storage, the knowledge base for storing information about plurality of objects and relationships there-between;   a relation exploiter, for extracting one or more of the objects from the knowledge base, responsive to their relationship to the object of interest;   a fusion engine, for receiving the sensor measurements from the gateway, the fusion engine comprising:
           an orchestrator module, for combining at least two of the sensor measurements, responsive to the relationships of the one or more objects to the object of interest and;   at least one task manager, for receiving a fusion task from the orchestrator module, for creating a fusion task data structure from the at least two combined sensor measurements, and for managing the fusion task data structure to identify the object of interest; and   a Bayesian fusion unit for performing the fusion task for the at least one task manager.   
               
 
         [0022]    It is another object of the present invention to provide the data fusion system as mentioned above, wherein the at least one task manager is a plurality of task managers. 
         [0023]    It is another object of the present invention to provide the data fusion system as mentioned above, wherein the knowledge base further contains a travel model of at least one of the plurality of objects. 
         [0024]    It is another object of the present invention to provide the data fusion system as mentioned above, further comprising a graph generator, for generating a graphical representation of the potential locations of the at least one object according to the travel model. 
         [0025]    It is another object of the present invention to provide the data fusion system as mentioned above, wherein the relation exploiter extracts one or more identifiers for the one or more objects from the knowledge base related to the object of interest. 
         [0026]    According to another embodiment of the present invention there is provided a computer implemented data fusion method for identifying an object of interest, the data from multiple data sources, the method comprising:
       receiving sensor measurements from a sensors set;   extracting one or more objects related to the object of interest from a knowledge base, the knowledge base comprising information about plurality of objects and relationships there-between;   managing at least one fusion task, responsive to the relationships of the one or more objects to the object of interest, the fusion task comprising fusing at least two of the sensor measurements into a data structure therefrom; and   using the data structure to identify the object of interest;
 
wherein at least one of fusion tasks comprising Bayesian fusion.
       
 
         [0031]    According to another embodiment of the present invention there is provided a non-transitory computer readable medium (CRM) that, when loaded into a memory of a computing device and executed by at least one processor of the computing device, configured to execute the steps of a computer implemented data fusion method for identifying an object of interest, the data from multiple data sources, the method comprising:
       receiving sensor measurements from a sensors set;   extracting one or more objects related to the object of interest from a knowledge base, the knowledge base comprising information about plurality of objects and relationships there-between;   managing at least one fusion task, responsive to the relationships of the one or more objects to the object of interest, the fusion task comprising fusing at least two of the sensor measurements into a data structure therefrom; and   using the data structure to identify the object of interest;
 
wherein at least one of fusion tasks comprising Bayesian fusion.
       
 
         [0036]    It is another object of the present invention to provide the data fusion method as mentioned above, wherein the knowledge base further contains a travel model of at least one of the plurality of objects. 
         [0037]    It is another object of the present invention to provide the data fusion method as mentioned above, further comprising generating a graphical representation of the potential locations of the at least one object according to the travel model. 
         [0038]    It is another object of the present invention to provide the data fusion method as mentioned above, further comprising extracting one or more identifiers for the one or more objects from the knowledge base related to the object of interest. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0039]    The subject matter disclosed may best be understood by reference to the following detailed description when read with the accompanying drawings in which: 
           [0040]      FIG. 1  is a conceptual block diagram of a system according to an embodiment of the present invention. 
       
    
    
       [0041]    For simplicity and clarity of illustration, elements shown in the FIGURE are not necessarily drawn to scale, and the dimensions of some elements may be exaggerated relative to other elements. In addition, reference numerals may be repeated among the FIGURE to indicate corresponding or analogous elements. 
       DETAILED DESCRIPTION 
       [0042]      FIG. 1  is a conceptual block diagram of a system  100  according to an embodiment of the present invention. A gateway  101  is an interface between a sensor set  103  and a fusion engine  105 . Sensors in sensor set  103  are labeled according to a scheme by which S t,i  represents a sensor of type t, where t=1, 2, . . . , N, for a total of N different sensor types; and i=1, 2, . . . , M, where M is the total number of sensors of type t. 
         [0043]    Gateway  101  is indifferent to sensor data and merely transmits sensor measurements  107  to fusion engine  105 , if a set of predefined rules  109  (such as conditions) is satisfied. Non-limiting examples of rule include: only observations in a predefined proximity to a certain object are transmitted to fusion engine  105 ; and only measurements with a confidence value above a predetermined threshold are transmitted to fusion engine  105 . In a related embodiment of the invention, this implements a push communication strategy and thereby, reduces internal communication overhead. 
         [0044]    Fusion engine  105  performs the actual fusion of sensor measurements  107 , and manages the creation and execution of fusion tasks. 
         [0045]    A knowledge base  111  contained in a non-transitory data storage containing information about objects of interest. Knowledge base  111  stores a travel model  113  of an object of interest, along with parameters of travel model  113 . Knowledge base  111  also contains map information and information about relationships between objects. 
         [0046]    A relation exploiter  121  extracts objects related to an object of interest from knowledge base  111 . In a related embodiment relation exploiter  121  extracts an identifier (non-limiting examples of which include a link or an ID) of objects related to the object of interest. 
         [0047]    A graph generator  123  provides a graphical representation of arbitrary map information, such as of potential locations of an object of interest according to travel model  113 . In a related embodiment, graph generator  123  pre-computes the graphical representation to reduce run-time computational load; in another related embodiment, graph generator  123  computes the graphical representation at run time, such as when it becomes necessary to update a map in real time. 
         [0048]    Gateway  101  transmits sensor measurements  107  to fusion engine  105 . Within fusion engine  105 , an orchestrator module  131  decides if a particular sensor measurement belongs to an already existing fusion task (such as a fusion task  151 , a fusion task  153 , or a fusion task  155 ) or if a new fusion task has to be generated. To assign a measurement to an active fusion task, an orchestrator module  131  compares and correlated the measurement with every active fusion task. Orchestrator module  131  can further merge fusion tasks, if it turns out that two or more fusion tasks are trying to identify the same object. Fusion tasks  151 ,  153 . and  155  are data structures, each of which store a class-conditional probability P(T i ), the probability that an object belongs to object class T i , with i=1, 2, . . . L, where L is the number of object classes. 
         [0049]    Fusion tasks  151 ,  153 , and  155  are managed by task managers  141 ,  143 , and  145  respectively, which maintain fusion task data, communicate with a Bayesian fusion unit  133 , and close their respective assigned fusion task at completion of identifying and/or classifying the object of interest. Bayesian fusion unit  133  performs the actual fusion calculations and hands back the results to the relevant task manager, for storage of the result in the appropriate fusion task. For compactness and clarity,  FIG. 1  illustrates three task managers  141 ,  143 , and  145  in a non-limiting example. It is understood that the number of task managers in an embodiment of the invention is not limited to any particular number, and that  FIG. 1  and the associated descriptions show three task managers  141 ,  143 , and  145  for purposes of illustration and explanation only and are non-limiting—a different number of task managers may be used as appropriate. 
         [0050]    For Bayesian fusion unit  133 , it is assumed that:
       the sensor measurements are conditionally independent; and   a miss-detection probability c f  is known.       
 
         [0053]    The first assumption is common in data fusion based on Bayesian inference. It allows recursive processing and thus reduces computational complexity and memory requirements. Knowing the miss-detection probability c f  is necessary; otherwise, it is not possible to improve the confidence value/class-conditional probabilities. 
         [0054]    Given class-conditional probabilities T i , i=1, 2, . . . L, stored in the selected fusion task, these probability values can be updated given the new sensor measurement of sensor S j  by means of Bayes&#39; theorem according to: 
         [0000]        P ( T   i   |S   j   =T   k )= c   n   ·P ( S   j   =T   k   |T   i )· P ( T   i )  (1)
 
         [0000]      with 
         [0000]        P ( S   i   =T   k   |T   i )= v   j ·δ ki   +c   f ·(1−δ ki )  (2)
 
         [0000]    where
       v j  is the confidence value of the measurement of sensor S j ;   δ ki  is Kronecker&#39;s delta (=1 when k=i, and =0 otherwise);   c f  is the miss-detection probability; and       
 
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         [0000]    is a normalization constant which ensures that all updated class-conditional probabilities P(T i |S j ), i=1, 2, . . . L sum to 1. 
         [0058]    The probability P(S j =T k |T i ) is the likelihood that sensor S j  observed object T k  given that the actual object is T i . If T k =T i  (that is, S j  has detected object T i , and therefore k=i), then Equation (2) evaluates to v j . On the other hand, if T k ≠T i  (that is, S j  has detected any other object than T i , and therefore k≠i), then Equation (2) evaluates to c f , indicating a miss-detection. The updated probability values are stored again in the appropriate fusion task. 
         [0059]    If a new fusion task needs to be instanciated for a given object, a task manager (such as task manager  141 ,  143 , or  145 ) retrieves the object&#39;s travel model  113  (e.g., kinematics such as velocity, steering angle, or acceleration of a car) and its parameters (e.g., maximum velocity and acceleration of a car) from knowledge base  111 . Thus, travel model  113  considers dynamic properties of the object and allows calculating, for instance, the maximum traveled distance within a given time interval. Travel model  113 , together with a graph, obtained from knowledge base  111  via a graph generator  123 , thereby represents the potential travel routes of the object, and allows Bayesian fusion unit  133  to estimate the most likely location of the object together with its class probability. If a sensor measurement does not directly correspond to the object, but is related to the object, orchestrator module  131  can exploit this relationship by means of relation exploiter  121  in order to assign the sensor measurement to the appropriate fusion task. In a non-limiting example, if the focus is on identifying a person in a shopping mall, even observations from a LPR can be of help, because knowledge base  111  can include a relationship between a car and the person who owns the car. Thus, having observed the car by means of a LPR system near the shopping mall can increase the evidence that the person in question is actually in the shopping mall. 
         [0060]    Benefits afforded by embodiments of the present invention include: 
         [0061]    Gateway  101  accepts data input from different sensor types without regard to their data  10  format, and provides flexibility and scalability in the number of sensors. 
         [0062]    Gateway  101  integrates rules  109  to moderate data transmission to fusion engine  105 , to ensure that sensor measurements  107  are sent to fusion engine  105  only when certain predetermined conditions are met. 
         [0063]    Embodiments of the invention exploit relationships between different objects and object types, corresponding to the integration of JDL level 2 data fusion, which is rarely currently realized. 
         [0064]    Embodiments of the invention orchestrate fusion tasks based not only on sensor measurements, but also on relationships between objects. 
         [0065]    Embodiments of the invention improve object identification by combining object relationships, object travel model  113 , graph generation for representing the environment, and Bayesian fusion. 
         [0066]    Multiple task managers  141 ,  143 , and  145  handle processing of fusion tasks in parallel, allowing flexibility and scalability in the number of fusion tasks that can be handled simultaneously in real time.