Abstract:
The system and method of the present application captures video of a scene in accordance with a plurality of capture characteristics, generates a capture profile for the video, and creates an index of the captured profile to enable a more rapid search of the captured video.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
       [0001]    The present application claims priority of U.S. Provisional Patent Application No. 61/667,547, filed Jul. 3, 2012, the content of which is incorporated herein by reference in its entirety. 
     
    
     FIELD OF THE DISCLOSURE 
       [0002]    The present application is related to the field of video display and management systems, and in particular, to optimizing the searching of surveillance video. 
       TECHNICAL BACKGROUND 
       [0003]    Effective video surveillance requires high quality images that can be viewed, searched, and analyzed. In many circumstances, images for people who are and were in an area may need to be known and identified, for viewing, searching and analyzing. Such video is often taken of various areas, and searching volumes of this video for particular individuals may be time consuming. 
       SUMMARY 
       [0004]    The system and method of the present application captures video of a scene in accordance with a plurality of capture characteristics, generates a capture profile for the video, and creates an index of the captured profile to enable a more rapid search of the captured video. 
         [0005]    In one embodiment, a non-transitory computer readable medium has stored thereon instructions for optimizing video surveillance that, when executed by a video system, direct the video system to identify a boundary in a scene, capture video of the scene, wherein the scene comprises objects, associate target objects with the boundary, create an index of the captured video, and search the captured video for the target objects based at least in part on the index. 
         [0006]    In another embodiment, a computerized, method of optimizing video surveillance comprises identifying a boundary in an area, capturing video of the area including a target object, analyzing the captured video of a scene of the area to generate an index for the video when the target object is adjacent the boundary, and searching for the target Object in the captured video based at least in part on the index. 
         [0007]    In yet another embodiment, a system may be provided which includes a capture interface configured to capture video of a scene including a target object and a predefined boundary object, and a processing system coupled with the capture interface and configured to analyze the captured video of a scene of the area to generate an index for the video when the target object is adjacent the boundary, and searching for the target object in the captured video based at least in part on the index. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]    Many aspects of the disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Moreover, in the drawings, like reference numerals designate corresponding, parts throughout the several views. While several embodiments are described in connection with these drawings, the disclosure is not limited to the embodiments disclosed herein. On the contrary, the intent is to cover all alternatives, modifications, and equivalents. 
           [0009]      FIG. 1  illustrates a block diagram of an embodiment of a video analysis system of the present application; 
           [0010]      FIG. 2  illustrates a flow diagram of an embodiment of a method of operation of a video analysis system of the present application; 
           [0011]      FIG. 3  illustrates a schematic diagram of an embodiment of a video surveillance environment of the present application; 
           [0012]      FIG. 4  illustrates a flow diagram of an embodiment of a method of operation of a video analysis module of the present application; 
           [0013]      FIG. 5  illustrates a flow diagram of an embodiment of operations in a surveillance environment. 
           [0014]      FIG. 6  illustrates a graphical user interface of an embodiment of the present application. 
           [0015]      FIG. 7  illustrates a graphical user interface of an embodiment of the present application. 
       
    
    
     DETAILED DESCRIPTION 
       [0016]    In the present description, certain terms have been used for brevity, clearness and understanding. No unnecessary limitations are to be applied therefrom beyond the requirement of the prior art because such terms are used for descriptive purposes only and are intended to be broadly construed. The different systems and methods described herein may be used alone or in combination with other systems and methods. Various equivalents, alternatives and modifications are possible within the scope of the appended claims. Each limitation in the appended claims is intended to invoke interpretation under 35 U.S.C. §112, sixth paragraph, only if the terms “means for” or “step for” are explicitly recited in the respective limitation. 
         [0017]      FIG. 1  illustrates video analysis system  101 . Video analysis system  101  includes processing system  103 , video interface  105 , memory  107 , and video analysis module  109 . Video analysis system  101  may be implemented in a number of ways, such as within or as part of a video recorder, a video camera, a client device, or as a stand-alone system. Video analysis system  101  operates as further described below to monitor and maintain the quality of captured video in surveillance settings. It should be noted that the processing system  103  of  FIG. 1  is capable of executing computer software and/or code in order to effectuate the operation of the system  101 , and any other processor method disclosed in this application. 
         [0018]    Referring to  FIG. 2 , process  200  is shown. Process  200  describes the operation of video analysis system  101 . To begin, processing system  103  associates a target object with a boundary (step  210 ). The boundary may be defined by a user through the system. The boundary may be a vertical or horizontal straight line placed over the scene captured by the camera, or may be a curved or other line defined by a user through a user interface. The boundary may be an area in a scene or room, such that a video camera may capture a clear image of a face of a person in the scene. The face shot, head shot, or other shot may be the target object. The target object may therefore be any defined object, as defined by a user in the scene. 
         [0019]    When a person crosses the boundary, processing system  103  may index where in the captured video the target object (e.g. face shot) is located. Later when face shots are queried by a user, the system can quickly search for all of the captured target objects (face shots). This greatly reduce the time needed to search the captured video for identification of persons who are or were in the area. If the target object is not found in the search of the indexed captured video, the rest of the captured video may be searched. 
         [0020]    Processing system  103  may obtain or otherwise access the video by way of video interface  105 , which itself may interface to a variety of video sources, such as a camera feed, stored video, or other video sources. 
         [0021]    Next, processing system  103  creates an index of the captured video (step  220 ). The index may be the portion of the captured video where a clear face shot is present within the scene captured. 
         [0022]    Finally, processing system  103  searches the captured video, and using the index, can quickly search the captured videos to identify people who are or were in the area (step  230 ). It should be noted that the system and method of the present application is not by itself a face recognition application, but a system and method that will index target objects (in this case face shots) for identification by another or another system. Of course, such a face recognition system could indeed be utilized in conjunction with, or be added to, the system and method of the present application. This may be performed in any number of ways, depending upon how video analysis system  101  is deployed. 
         [0023]      FIG. 3  illustrates video surveillance environment  300  in another embodiment. Video surveillance environment  300  includes video capture device (VCD)  301 , VCD  303 , and VCD  305 . VCDs  301 ,  303  and  305  communicate over video network  307  with recorder  309  and client device  311 . As shown, VCD  301  generally captures video of scene A, which includes target object  313  and object  315 . Likewise, VCD  303  generally captures video of scene B, which includes target object  317  and object  315 . VCD  305  captures video of scene C, which includes target object  321  and object  315 . 
         [0024]    VCD  301  is shown with video analysis module (VAM)  331 , the operation of which will be described in more detail below with respect to  FIG. 3 . VAM  331  is capable of associating target object  313 ,  317 ,  321  with the boundary object  315  and creating an indication where within the captured video the target object is shown. VAM  331  is capable of searching the captured video quickly, only searching the captured video that have target objects  313 ,  317 ,  321 . In this manner, search times for face shots of people who are or were in the area may be greatly reduced. 
         [0025]    It should be understood that VAM  331  could be implemented within VCD  303  and VCD  305 , as well as within VCD  301 . It should also be understood that similar video analysis modules could be implemented within other elements of video surveillance environment  300 , such as with recorder  309 , client device  311 , within other element on video network  307  not shown, or as a stand-alone element. VAM  311  may be implemented as a software component, a hardware component, firmware, or any combination thereof. 
         [0026]    Client device  311  may be capable of allowing a user to define the boundary. This is then used by VAM  331 , such that when a person crosses the boundary, the general position or time within the captured video where this took place can be indexed and analyzed. VAM  331  may then search the captured video, in this example, stored on recorder  309  to quickly find a target object  313 ,  317 ,  321 . 
         [0027]    Referring again to  FIG. 4 , a graphical depiction of the operation of VAM  331  is provided. Only a single video analysis module is shown handling video of scenes A, B, and C, although it should be understood that, depending upon the chosen architecture, multiple video analysis modules  331  may be involved. 
         [0028]    Continuing with  FIG. 4 , VAM  331  receives video of scenes A, B, and C. As shown to the left of VAM  331 , the video initially video captured by VCD  301  of scene A includes objects  313  and  315 . VAM  331  generates an index  350  or any other indication that an object (a person) is adjacent or has crossed another object  315  (the boundary). VAM  331  may then associate the objects, and create an index  350  or list or other notation of where a search may find a relatively good image of the objects. 
         [0029]    Still referring to  FIG. 4 , VAM  331  produces results similar to those of scene A respect to scene B and scene C when the same operations are performed. As shown to the left of VAM  331 , objects  317  and  321  represent different persons. The location in the captured video when the person crossed the boundary is noted for faster searching later when needed or desired. 
         [0030]      FIG. 5  illustrates several different implementations, each an example of a deployment of VAM  331 . Various data flows are described relating to the various implementations of VAM  331 . It should be understood that  FIG. 5  is merely illustrative and non-limiting and does not encompass all the possible deployments of VAM  331 . 
         [0031]    In the first implementation represented by the solid line, VAM  331  is implemented in VCD  301 . It should be understood this example could apply as well to VCD  303  and VCD  305 . In this situation. VCD  301  executes VAM  331  to associate potential target objects  313 ,  317 ,  319  with a boundary  315 , note where in the captured video that occurs, and be capable of relatively rapidly searching the captured video for a target object. After searching, the target object(s)  313 ,  317 ,  319  may be displayed on client station  311 . The captured video to be searched may resides on recorder  309  or any other location capable of performing that function. 
         [0032]    As represented by the dashed line in  FIG. 5 , VAM  331  could also be implemented in recorder  309 . In this case, unmodified video is initially transferred from VCD  301  to recorder  309 . An alternative, recorder  309  could implement the analyzing, and indexing itself, as opposed to instructing VCD  301  to make the modifications. In a final example, VAM  311  is implemented in client device  311 , as represented by the dotted lines. In this case, unmodified video is initially transferred from VCD  301  to client device  311 . 
         [0033]    Referring now both to  FIGS. 6 and 7 , a graphical user interface (GUI)  500  is illustrated. It should be noted that this GUI  500  is exemplary of the system and method of the present application, and that other embodiments and screen shots could also be utilized to illustrate the workings of the system and method of the present application. Here, an exemplary image search screen  504  is illustrated including index images  502  from the system and method described above. These indexed images  502  include both the indexed image  512  as well as selective buttons  514 .  FIG. 6  illustrates the results of an index search conducted by a user, resulting in the found indexed images  502 . A user may utilize the selective buttons  514  in order to play the selected piece of video corresponding to the image  512 , or to remove the image  512 , or to add the image  512  to the storyboard  506 . The user may do that with all of the indexed images  502  found in an index search. The storyboard  506  allows a user to collect a number of index images  502  and put them in order to follow a particular individual through a number of different indexed images  502 , for example. The image search screen  504  also includes camera folders  508  that organize a large number of video capture devices and a camera list  510  for each of the camera folders  508 . Referring specifically to  FIG. 7 , the image search screen  504  includes a search query menu  516  that allows the user to set a particular and specific set of search criteria that will produce the indexed images  502  of  FIG. 6 . Again, the search query menu  516  as well as the image search screen  504  generally, may be organized, embodied and/or implemented in a number of different ways, and the GUI  500  of  FIGS. 6 and 7  are not intended to be limiting to this particular embodiment. 
         [0034]    The included descriptions and figures depict specific embodiments to teach those skilled in the an how to make and use the best mode. For the purpose of teaching inventive principles, some conventional aspects have been simplified or omitted. Those skilled in the art will appreciate variations from these embodiments that fall within the scope of the invention. Those skilled in the art will also appreciate that the features described above can be combined in various ways to form multiple embodiments. As a result, the invention is not limited to the specific embodiments described above, but only by the claims and their equivalents. 
         [0035]    In the foregoing description, certain terms have been used for brevity, clearness, and understanding. No unnecessary limitations are to be inferred therefrom beyond the requirement of the prior art because such terms are used for descriptive purposes and are intended to be broadly construed. The different configurations, systems, and method steps described herein may be used alone or in combination with other configurations, systems and method steps. It is to be expected that various equivalents, alternatives and modifications are possible within the scope of the appended claims.