Abstract:
In an approach to tracking at least one target subject in a camera network, a search is started to find a target subject on a camera within a camera network. Features are extracted from the target subject and search queries are initiated in other nearby cameras within the camera network. Search queries attempt to detect target subjects and present the finds in a ranked order. Application of aggregate searches in multiple cameras and prior search results are used to improve matching results in the camera network; propagate a search of the target subject to discover the full pathway in the camera network; and project future occurrences of the target subject in subsequent cameras in the camera network.

Description:
BACKGROUND OF THE INVENTION 
     The present invention relates generally to information processing techniques and more specifically to discovering object pathways of a target subject in a camera network. 
     Digital videos obtained from surveillance cameras are sources of data to perform investigations such as finding vehicles; searching for and finding people with specific attributes; detecting abandoned packages; and detecting traffic violations. These tasks are typically achieved by manual browsing or by employing commercial video analytic solutions. Investigators and video analysis personnel carry out these tasks by individually reviewing any relevant camera devices in a surveillance camera network. 
     SUMMARY 
     According to one embodiment of the present invention, a method for tracking at least one target subject in a camera network, the method comprising the steps of: receiving, by one or more processors, a selection of at least one target subject in a first image taken by a first camera from a user; populating, by one or more processors, one or more databases with one or more features of the at least one selected target subject; initiating, by one or more processors, one or more search queries to a set of cameras within a camera network near the first camera, wherein the one or more search queries are used to determine, in an iterative manner, whether at least one of the one or more features of the at least one selected target subject is present in at least one image from at least one camera of the set of cameras; responsive to determining at least one of the one or more features is present in at least one image from at least one of the set of cameras, analyzing, by one or more processors, a set of results received from the one or more search queries to determine whether the at least one selected target subject in the first image is present in one or more images from the set of cameras; and responsive to determining the at least one selected target subject is present in one or more images from the set of cameras, propagating, by one or more processors, a search of the at least one selected target subject within the set of cameras within the camera network. 
     Another embodiment of the present invention provides a computer program product for tracking at least one target subject in a camera network based on the method described above. 
     Another embodiment of the present invention provides a computer system for tracking at least one target subject in a camera network, based on the method described above. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional block diagram illustrating a data processing environment, in accordance with an embodiment of the present invention; 
         FIG. 2  is a flowchart depicting the operational steps of an analytics module for recovering a pathway of a subject in a camera network, in accordance with an embodiment of the present invention; 
         FIG. 3  is a flowchart depicting the operational steps of the analytics module for accumulating re-ranking of search results using aggregated search results, in accordance with an embodiment of the present invention; 
         FIG. 4  is a depiction of the flow of data during operation of the analytics module while searching for a pathway of a subject in the camera network, in accordance with an embodiment of the present invention; and 
         FIG. 5  depicts a block diagram of internal and external components of a computing device, such as the computing device of  FIG. 1 , in accordance with an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Law enforcement, investigative, academic, or other personnel may examine video or images retrieved from multiple cameras during the course of investigations. Efforts to focus on achieving exact object-to-object matches across camera views are time-consuming and may not be accurate enough to establish reliable object associations. For example, false negatives (e.g., missing detections/incorrect matches) are particularly not favored during criminal investigations. Embodiments of the methods and systems for the present invention assist the investigation process by narrowing the search range of cameras examined and semi-automatically or automatically speeding up the forensic analysis. Embodiments of the present invention focus on discovering the pathways of target subjects in a surveillance camera network, where an operator of a camera network initiates the search by locking in on a target subject in one camera and propagating the target subject&#39;s pathway to neighboring cameras. 
     The present invention will now be described in detail with reference to the Figures.  FIG. 1  is a functional block diagram illustrating a data processing environment, generally designated  100 , in accordance with one embodiment of the present invention.  FIG. 1  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Modifications to data processing environment  100  may be made by those skilled in the art without departing from the scope of the invention as recited by the claims. In this exemplary embodiment, data processing environment  100  includes computing device  122  and camera devices  105 A to  105 N. 
     Camera devices  105 A- 105 N are connected to analytics module  120  on computing device  122 . Camera devices  105 A- 105 N record images, using known methods and functionalities, as individual still images or videos. The individual still images or videos can be stored locally on a camera device  105 A- 105 N, on computing device  122 , transmitted to another location (i.e., another computing device), via a network, or both. The number of camera devices working in conjunction with analytics module  120  vary in different embodiments. 
     In another embodiment, camera devices  105 A- 105 N connect to computing device  122  via a network. The network can be, for example, a local area network (LAN), a wide area network (WAN) such as the Internet, or a combination of the two, and can include wired, wireless, or fiber optic connections. In general, the network can be any combination of connections and protocols that will support communication between computing device  122  and camera devices  105 A- 105 N. In an embodiment, a network can include a cloud computing network. 
     Computing device  122  serves as a host to analytics module  120 . A computing device  122  may be a laptop computer, a tablet computer, a netbook computer, a personal computer (PC), a desktop computer, a personal digital assistant (PDA), a smart phone, a thin client, or any programmable electronic device capable of executing machine readable program instructions and communicating with various components and devices within data processing environment  100 . The computing device may include internal and external hardware components, as depicted and described in further detail with respect to  FIG. 5 . 
     Analytics module  120  includes feature extraction module  115 , database  125 , and user interface  150 . In this exemplary embodiment, each of these components reside on the same computing device. In other exemplary embodiments, the components are deployed on one or more different computing devices within data processing environment  100  such that camera devices  105 A- 105 N can work in conjunction with feature extraction module  115 , database  120 , and user interface  150 . 
     Feature extraction module  115  is connected to camera devices  105 A to  105 N; feature database  140 ; and prediction adjustment module  136 . Feature extraction module  115  extracts features from a target subject found on a camera device or camera devices. Feature extraction module  115  can detect, track moving objects, and extract features of moving objects. 
     Database  125  includes data retrieving and ranking module  130 ; feature weight adjustment module  132 ; correlation refinement module  134 ; prediction adjustment module  136 ; feature database  140 ; and correlation database  142 . Database  125  serves as a repository of features and correlations of target subjects found on images or video retrieved from camera devices  105 A- 105 N. A database is an organized collection of data. Database  125  can be implemented with any type of storage device capable of storing data and configuration files that can be accessed and utilized by computing device  122 , such as a database server, a hard disk drive, or a flash memory. 
     Data retrieval and ranking module  130  uses the data from feature database  140 . Data retrieval and ranking module  130  retrieves features in the form of data to be processed from correlation database  142 , populates search queries to locate relevant features in neighboring cameras, and generates a ranking using weighted correlations with respect to located features. Data retrieval and ranking module  130  is connected to user interface  150 ; feature weight adjustment module  132 ; and feature database  140 . The features are sent to feature database  140  to be later processed by data retrieval and ranking module  130 . 
     Feature weight adjustment module  132  is connected to user interface  150  and data retrieval and ranking module  130 . Operational steps of feature weight adjustment module  132  are used in the estimation of object matching across cameras. Object matching across two cameras takes into consideration a correlation weight and a feature weight in a query. Correlation weights are weights of the feature-based correlation models between two cameras. One or more feature sets may include color similarity, overlapping of facial attributes, spatiotemporal proximity, etc. Feature sets are incorporated into feature-based correlation models in order to determine what the distinctive features of a target subject are and analyze images across multiple cameras. For example, in an indoor camera network where the lighting is ambient and stable, the color information in different cameras can be considered relatively similar (e.g., a person wearing red would appear “red” in both cameras A and B). Since there might be several different hallways or staircases between cameras A and B, the spatiotemporal proximity might not be as distinctive as the color similarity model. In another situation such as an outdoor highway setting where the lighting is dynamic but the traveling path is constrained, the spatiotemporal proximity plays a more important role than the color similarity. A feature weight in a query is the weight used to determine the distinctive features of a target subject. For instances of a target subject observed with the facial attributes of having a “beard” across multiple cameras, the facial attributes would have higher weight in the search query. In another instance, a target subject may wear or take off a red jacket from one camera to another. Thus, the color is not a distinctive feature of the target subject and color matching would have a lower weight in the search query. 
     Correlation refinement module  134  obtains correlation data and sends the obtained correlation data to correlation database  142 . Based on an operator&#39;s configuration of correlation models, analytics module  120  applies the correlation model to the target subject. Correlation data is obtained from the target subject upon application of the correlation model. Correlation data are derived from features (e.g., spatial trajectory, color characteristics, detected sizes, or other features supported by the camera network) of a target subject observed in at least two cameras. Correlation refinement module  134  is connected to user interface  150  and correlation database  142 . 
     Prediction adjustment module  136  is connected to user interface  150  and feature extraction module  115 . Analytics module  120  performs queries to find a target subject in cameras within the camera network. Visual images deemed to be a “match” by the operator from the queries are compiled in aggregate by analytics module  120 . The data obtained from prediction adjustment module  136  is sent to feature extraction module  115 . Whether a “next camera” is in a “search” mode or a “detection” mode depends on the spatiotemporal projection of the target subject&#39;s occurrence. In more undefined situations pertaining to spatiotemporal projections of the target subject&#39;s occurrence, both search and detection refinements are carried out to enhance search/detection effectiveness and efficiency. 
     Feature database  140  is connected to and communicates with data retrieval and ranking module  130  and feature extraction module  115 . Feature extraction module  115  obtains feature data from camera devices  105 A to  105 N and sends the obtained feature data to feature database  140 . Operators focus on a certain feature and/or certain features of a target subject and select them as being pertinent for finding the target subject in other cameras devices. Data retrieval and ranking module  130  works in conjunction with feature module  115  in order consider data from feature and furthermore compute a ranking of visual images deemed to be a match. 
     Correlation database  142  is connected to and communicates with data retrieval and ranking module  130  and correlation refinement module  134 . Correlation refinement module  134  obtains correlation data and sends the obtained correlation data to correlation database  142 . The likelihood of a target subject in a second camera to be the same target subject who appeared in the first target depends on a feature set of a target subject in respective camera views and correlation between the observed features in the first and second camera. A correlation model is configured by an operator via user interface  150 . In one embodiment, the correlation model is based on color is a histogram intersection. In another embodiment, the correlation model is based on proximity using spatiotemporal prediction. Correlation data pertains to the correlation between features obtained from a visual image deemed to be a target subject observed within two cameras. Correlation refinement module  134  is connected to user interface  150  and correlation database  142 . 
     User interface  150  may be for example, a graphical user interface (GUI) or a web user interface (WUI) and can display text, documents, web browser windows, user options, application interfaces, and instructions for operation, and includes the information (such as graphics, text, and sound) a program presents to a user and the control sequences the user employs to control the program. User interface  150  is capable of receiving data, user commands, and data input modifications from a user and is capable of communicating with modules and components within data processing environment  100 , such as analytics module  120 , data retrieval and ranking module  130 ; feature weight adjustment module  132 ; correlation refinement module  134 ; and prediction adjustment module  136 . In various embodiments of the present invention, a user interacts with user interface  150  in order to provide feedback to analytics module  120 . 
     While  FIG. 1  depicts analytics module  120  including a plurality of modules for operation of the invention, one of skill in the art will understand that each of the functions and capabilities of each of the modules may be performed by analytics module  120  on computer  122 . 
       FIG. 2  is a flowchart depicting the operational steps of analytics module  120  for recovering a pathway of a subject in a camera network, in accordance with an embodiment of the present invention. 
     In step  205 , analytics module  120  initializes cross camera correlation. At an initial point, cross camera correlations are initialized using only existing domain knowledge via correlation refinement module  134 . The existing domain camera coordinates in real-world terms are known (e.g., GPS coordinates and/or floor-plan coordinates). Auxiliary information such as camera orientation can also be part of the initial input to the camera network. Camera correlations are primarily estimated based on their real-world proximities. For example, if the distance from camera A to camera B is 100 meters, analytics module  120  can assume it would take approximately 1 minute for a person to walk from camera A to camera B. In this embodiment, spatial proximity correlation would have a higher weight over other features (e.g., color similarity) while performing cross camera correlation. 
     In step  210 , analytics module  120  determines a target subject from Camera A. The camera network described in  FIG. 2  includes at least two cameras—Camera A and Camera B. When a target subject (e.g., a person) is locked in camera A by analytics module  120 , features such as trajectory data, moving speed, cloth color, etc. of the target subject are extracted using known analytics methods from the still image or video and sent to database  125  (and more specifically to feature database  140 ). Analytics module  120  determines a target subject based, at least in part, on input from a user via user interface  150 . In another embodiment, a user selects features from the still image or video recorded by camera A and input the features to be used by analytics module  120 . 
     In step  215 , analytics module  120  retrieves objects with queries. Correlation weights are the weights of feature-based correlation models between two cameras. Feature weights are the weights to determine the distinctive features of a target object. The correlation weights and the features weights are configured by an operator. In various embodiments, the objects retrieved may be target subjects or features of the target subjects. Search queries can be automatically populated using extracted features to retrieve relevant objects in neighboring cameras. In another embodiment, a search query may be automatically populated with a target subject. Weight is given to a feature based on how distinctive a feature is as opposed to how readily a feature can be changed. For example, if an image is derived from neighboring cameras, the found target subject may not always have on his jacket, suggesting that this feature is not “distinctive” enough to be a valid indicator for searching and thus given less weight. In conjunction with correlation refinement module  134  and feature weight adjustment module  132 , variables such as a feature set, correlation models, feature correlation, and feature weight are configured by an operator. The correlation weights used to search in different cameras can be different. For example, in some camera pairs, spatial proximity works better, while for other camera pairs, color similarity works better. In certain instances, a particular type of correlation model would be more appropriate. Target subjects within a highway settings have dynamic lighting within a constrained traveling path and thus using spatiotemporal proximity within a correlation model is more distinctive than a color-based correlation model. In some embodiments, all of the features extracted from a target subject will be put into a query. In other embodiments, only certain distinctive features extracted populate a search query. Different features can have different weights within the same query. Certain cameras capture specific features and visual images differently than others (i.e., red in one camera is bright while red in a second camera is dark). The operator can program the correlation model to account for differences in appearances of visual images in different cameras by configuring different features as having different weights within the same query. 
     Analytics module  120  invokes correlation refinement module  134  to determine the likelihood—L—of a target subject Y′ found in camera B to be the same target subject Y which previously appeared in camera A, can be described using the equation, Eq. 1, below:
 
 L ( Y→Y′:F   Camera A ( Y ){circle around (×)} F   Camera B ( Y ′))  (Eq. 1)
 
where F is the observation (feature set) of a target Y or target Y′ in its respective camera views and {circle around (×)} is a general notation for correlation between the observed features in two cameras. For example, features of an observed person within a camera network include spatial trajectory, color characteristics, moving speed, entry/exit points of the person, facial attributes (e.g., bold), detected sizes, and other features that a camera network supports. Elements (e.g., features) can be added to and removed from F by reconfiguring the existing algorithm. A correlation model based on color may be a histogram intersection. A correlation model may be a proximity using spatiotemporal prediction. Eq.1 allows for a way of quantifying L. The single values for L which are generated can be recited as absolute values or a set of normalized values.
 
     Analytics module  120  invokes feature weight module  132  to determine a feature weight when matching a set of two observations across two cameras. Both the correlation weight and the feature weight are taken into consideration in combination with the correlation models allowing for the searched results to be ranked accordingly. Correlation weights are the weights of the feature-based correlation models between two cameras and configured by an operator. A ranking is computed by taking a linear combination of all correlation models weighted by the product of the correlation weight and the feature weights. The equation, Eq. 2 below, utilized by analytics module  120  is:
 
 E=Sum   i→n ( w   c   i   w   f   i   F   i )  (Eq. 2)
 
where E is the rank measure; n is the number of correlation models; w c   i  is the correlation weight of the correlation using a feature i; w f   i  is the feature weight of the feature i for the current searched target subject; and F i  is the feature correlation model between the two cameras using a feature i (e.g., color similarity, overlap of facial attributes, spatiotemporal proximity, and etc.).
 
     In step  220 , analytics module  120  ranks objects according to weight correlation. Objects are a superset which contain the target subject. For example, if the target subject is a person, the “objects” returned from searches may be a group of people which satisfy the search criteria to some extent and are ranked based on their relevance. In addition, there may be “noise” (e.g., non-human “search results” detected as people) in the target subjects. The objects from neighboring cameras are retrieved and ranked according to the weighted correlations for all relevant features using Eq. 2 via analytics module  120 . Eq. 2 computes the E value which gives objects a ranking value. In an embodiment, an operator performs an assessment on the ranked results and determines the correct match of the target subject. The selected match is considered a positive match and is fed back to the system to enhance the correlation model of the respective camera pair. 
     In step  225 , analytics module  120  initiates search queries iteratively. After discovering each occurrence of the same target subject in subsequent cameras, the analytics module  120  takes information from each such occurrence and refines its search queries in the next steps. Such a search process is continued until the full pathway of the target subject is recovered in the camera network. Analytics module  120  can also take the historical search information to make predictions of target subject occurrences in cameras within the camera network. Utilizing the steps discussed above, analytics module  120  can recover the full pathway of a target in the camera network. 
       FIG. 3  is a flowchart depicting the operational steps of analytics module  120  for accumulating re-ranking using aggregated search results, in accordance with an embodiment of the present invention. 
     In this exemplary embodiment, analytics module  120  is not achieved by isolated camera-pairs and the camera network includes cameras A, B, C, and D. For example, a target subject—target X—travels from camera A to cameras B, C, and D in a temporal sequential order. Other solutions generally establish the association first from camera A to camera B, then from camera B to camera C, and finally from camera C to camera D. Analytics module  120  uses previously discovered information from camera A, camera B, and camera C to infer the occurrence of target X in camera D. This generates aggregated search results of the current target X and re-ranks the searched events in subsequent cameras. Simple spatiotemporal proximity and color similarity are the correlations in cameras A, B, C, and D. In other embodiments, different feature sets and correlation models can be adopted and integrated into analytics module  120 . 
     Using the aggregated search results, analytics module  120 , using prediction adjustment module  136 , automatically adjusts video analytics in one or more camera views where the target subject is expected to appear. This applies to situations where the occurrence of the target subject in a next camera has not yet happened at the time when the search is taking place. For example, a target subject travels through cameras A, B, C and D. All four cameras (A, B, C, and D) are able to detect facial attributes. Each camera has an analytic component which detects facial attributes configured with a set of default parameters. The target subject has already appeared in cameras A, B, and C, but not yet in camera D. The operator has successfully found and confirmed to analytics module  120  via feedback occurrences of the target subject in cameras A, B, and C and the corresponding features are also retrieved by analytics module  120 . During an accumulative re-ranking using aggregated search results, analytics module  120  has assessed that this target subject has a facial attribute of a “beard” in cameras A, B, and C. After adjusting the feature weight, prediction adjustment module  136  determines that the facial attribute of a “beard” is a very distinctive feature. Therefore, the analytic component in camera D (the camera where the target has not appeared in but may in the future) automatically adjusts its sensitivity parameters to “focus” more on detecting a “beard”. The default analytic parameters on camera D before the adjustment may not be sensitive enough and the true appearance of the target may possibly be missed or put in a low rank. After the analytic adaptation, it is more likely that the target subject&#39;s facial attributes can be discovered, and thus decrease the likelihood of misdetection (i.e., false negatives). The ranking model utilized for identifying paths of the multiple target subjects can be adjusted. 
     In step  305 , analytics module  120  finds a target X with a feature Z on camera A. In this exemplary embodiment, the occurrence of target X with feature Z in cameras B, C, and D are primarily based on the time, and based on physical proximity, it takes for movement from camera A to camera B; camera A to camera C; and camera A to camera D. In this exemplary embodiment, the feature weight of spatiotemporal correlation is the dominant term for generating a ranking. 
     In step  310 , analytics module  120  presents the ranked results (to an operator). In this exemplary embodiment, an operator is presented with the ranked search results and the operator discovers the same target X with the same feature Z in the results. The operator manually specifies the match as a “true occurrence” of target X with feature Z assuming the feature Z is also found on Camera B. 
     In step  315 , analytics module  120  feeds features from camera A and camera B to feature extraction module  115 . In this exemplary embodiment, analytics module  120  receives the operator&#39;s response and analytics module  120  feeds observations (i.e. features) of target X in cameras A and B to feature extraction module  115 . At this point, since feature Z has been observed in both occurrences, a feature weight for a particular feature is increased. For example, if feature Z is a red hat, the feature weight for color is increased, and analytics module  120  has an increased confidence level in using color for matching. 
     In step  320 , analytics module  120  feeds the feature weights of target X back to the system. In this exemplary embodiment, analytics module  120  re-learns the feature weights of target X and this is used as an additional input by analytics module  120 . 
     In step  325 , analytics module  120  re-ranks queried results in cameras. In this exemplary embodiment, the queried results are re-ranked in subsequent cameras in a temporal sequence. The updated feature weights which are employed by analytics module  120  improve the relevance of the results generated. In addition to the updated feature weights, the aggregated search results can also update the correlation computation. For example, the projected occurrence time of the target in camera D can be refined using observations in multiple cameras compared to using the starting camera A. 
       FIG. 4  is a depiction of the flow of data during operation of analytics module  120  while searching for a pathway of a subject in the camera network, in accordance with an embodiment of the present invention. 
     Analytics module  120  obtains camera input  405 . Camera input  405  can be data which takes the form of correlation models and a visual image. The visual image is found on a camera device or camera devices, such as camera devices  105 A to  105 N, and is the “target subject” of interest to an operator. Correlation models (which are described above) may take into account a single factor or multiple factors such as spatial temporal proximity and color schematics. 
     Analytics module  120  obtains feature extraction data  410  (i.e., features) of the target subject. These features of the target subject are treated as data amenable for further processing by analytics module  120 . Feature extraction data  410  may include a single feature or multiple features of the target subject. 
     Repository  415  contains feature repository  417  and correlation repository  419 . Feature extraction  410  data  410  is stored in feature repository  417 . Correlation data obtained from camera input  405  data are sent to correlation repository  419 . 
     Analytics module  120  configures the variables of Eq.1 and Eq.2, as query formulation  420  data in  FIG. 4 , in order to find the likelihood of matching targets and the ranking of images, respectively. Data from feature repository  417  can alone be incorporated into a query formulation to furnish query formulation  420  data. 
     Analytics module  120  generates the results from Eq.1 and Eq. 2, represented as resultant retrieval and rank  425  data, to output to a user the likelihood of matching target subjects and the ranking of images. The resultant retrieval and rank  425  data can be determined directly from correlation repository  419  (and by-passing query formulation  420  data). 
     To obtain operator interaction  430  data, analytics module  120  determines if the operator finds the target subject. The operator analyzes resultant retrieval and rank  425  data. 
     If the operator determines that the target subject has not been found, then no further steps are taken on resultant retrieval and ranking  425  data. Analytics model  120  no longer processes the data and is now in cessation of process  435 . 
     If the operator determines that the target subject has been found, then analytics module  120  applies analytics solution  440 . Analytics solution  440  may be result  442 , result  444 , result  446 , and result  448 . 
     Analytics module  120 , utilizing feature weight result  448  in analytics solution  440 , initiates adjustment of the “feature weight data” while recalculating results in resultant retrieval and ranking  425 . The variable w c   i  and w f   i  can be reconfigured and thus potentially altering the E value of Eq. 2. Feature weights determine what the distinctive features of the target subject are. If a distinctive feature is found by an operator, then the distinctive feature found by the operator may be reconfigured to have a higher w f   i  value. 
     Analytics module  120 , utilizing found targets result  446  in analytics solution  440 , initiates the sending of “found targets data” for query formulation  420 . The process is repeated by taking into account the new found target subject data. Eq. 1, which finds the likelihood of a target in a second camera as being the same target in the first camera analyzed, is recalculated. The feature sets and correlation between the observed target features amongst the two cameras can be reconfigured. Thus, the L value of eq. 1 can be altered upon reconfiguring the feature sets and correlation variables. 
     Analytics module  120 , utilizing correlation refinement result  444  in analytics solution  440 , initiates “correlation refinement data” to correlation database  419 . Correlation refinement data flows through an interactive system by automatically populating query formulations and ranking queried results based on correlation models and feature weights. Additionally, the operators may provide positive training samples by manually marking object matches across cameras. In an exemplary embodiment, only prior knowledge on a cameras&#39; locations in real-world terms is processed. The camera-to-camera correlations is dominated by the spatiotemporal proximity. When the operator searches for occurrences of a target subject from one camera to another camera, the queried results are ranked based on the proximity of the target subject&#39;s occurrence time in one camera and its projected occurrence time in another camera. Other feature models such as color and face attributes do not play much of a role in this ranking because there is no prior knowledge in these correlation models. When the operator marks a match of the target subject between one camera and another camera, the target subject&#39;s observations are used as the positive training to refine the correlation models and correlation weights. For example, a target subject&#39;s color is perceived as “red” in one camera and as “dark orange” in another camera. The operator has manually determined that these two objects are the same person and is implying that there is a mapping from “red” to “dark orange” from one camera to another camera, respectively. Thus, the color correlation model is enhanced by this new positive sample. In addition, since a correlation is found in the color spectrum, the correlation weight for that color model is also increased. By repeating the search process with an operator&#39;s input, analytics module  120  can iteratively enhance the cross camera correlations in the network and learn the weights of these feature correlations. 
     Analytics module  120 , utilizing target predication result  442  in analytics solution  440 , initiates “target prediction” and sends the data from the target subject prediction to feature extraction  410 . Result  442  allows for the automatic adjustment of the video analytics in camera views where a target subject is expected to appear. This applies to situations where the occurrence of the target subject in the next camera has not yet happened at the time during which the search is actively being conducted. For example, a target subject is captured by camera A, camera B, and camera C and not yet in camera D. The features from camera A, camera B, and camera C are retrieved after finding the target subject occurrence in camera A, camera B, and camera C. Eventual accumulative re-ranking using aggregated search results leads to focusing on a key feature or set of key features. The feature can be adjusted to get camera D to focus more on the key feature or set of key features in order to facilitate the finding of the target subject from camera A, camera B, and camera C in camera D. 
       FIG. 5  depicts a block diagram of components of a computing device, such as computing device  122 , generally designated  500 , in accordance with an illustrative embodiment of the present invention. It should be appreciated that  FIG. 5  provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environment may be made. 
     Computing device  500  includes communications fabric  502 , which provides communications between computer processor(s)  504 , memory  506 , persistent storage  508 , communications unit  510 , and input/output (I/O) interface(s)  512 . Communications fabric  502  can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric  502  can be implemented with one or more buses. 
     Memory  506  and persistent storage  508  are computer readable storage media. In this embodiment, memory  506  includes random access memory (RAM)  514  and cache memory  516 . In general, memory  506  can include any suitable volatile or non-volatile computer readable storage media. 
     Program instructions and data used to practice embodiments of the present invention may be stored in persistent storage  508  for execution and/or access by one or more of the respective computer processors  504  via one or more memories of memory  506 . In this embodiment, persistent storage  508  includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage  508  can include a solid state hard drive, a semiconductor storage device, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information. 
     The media used by persistent storage  508  may also be removable. For example, a removable hard drive may be used for persistent storage  508 . Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage  508 . 
     Communications unit  510 , in these examples, provides for communications with other data processing systems or devices. In these examples, communications unit  510  includes one or more network interface cards. Communications unit  510  may provide communications through the use of either or both physical and wireless communications links. Program instructions and data used to practice embodiments of the present invention may be downloaded to persistent storage  508  through communications unit  510 . 
     I/O interface(s)  512  allows for input and output of data with other devices that may be connected to computing device  500 . For example, I/O interface  512  may provide a connection to external devices  518  such as a keyboard, keypad, a touch screen, and/or some other suitable input device. External devices  518  can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention, e.g., software and data, can be stored on such portable computer readable storage media and can be loaded onto persistent storage  508  via I/O interface(s)  512 . I/O interface(s)  512  also connect to a display  520 . 
     Display  520  provides a mechanism to display data to a user and may be, for example, a computer monitor. 
     The programs described herein are identified based upon the application for which they are implemented in a specific embodiment of the invention. However, it should be appreciated that any particular program nomenclature herein is used merely for convenience and thus, the invention should not be limited to use solely in any specific application identified and/or implied by such nomenclature. 
     The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.