Abstract:
A video monitoring and analysis system detect subjects when they are entering and/or exiting from a room. The system enables a user to define a portal, such as doorway of the room. The system then monitors the movement of foreground objects in the room. Objects that appear only in the portal are classified as passing by the portal, e.g., doorway. Objects that initially appear in the portal and then are detected moving within the room are classified as having entered the room. Objects that are in the room and then disappear within the portal are classified as having exited the room. The system further has provisions for generating real-time alerts and performing forensic searches.

Description:
BACKGROUND OF THE INVENTION 
       [0001]    Video systems used for surveillance are becoming increasingly common. They can be used to monitor buildings and rooms within buildings. The video systems can also be used monitor public areas such as parks or public spaces in and around buildings. In some cases they are used to catch illegal activity. In other cases, they are used to monitor usage such as shopping patterns in a retail store or traffic levels on public roadways, or detect events that would require the intervention of emergency response personnel. 
         [0002]    One trend is to offer increasing levels of analytical capabilities. These analytical capabilities are helpful in a number of ways. They can be employed to reduce the number of operators required to monitor the video feeds from the separate cameras of the video systems by automatically notifying operators when events occur that require some action. For example, they can notify operators when individuals are entering a secure area or when potentially illegal activities are taking place, in real time. On the other hand, the video systems can further store metadata with the video data generated by the cameras so that operators can later search through the mountains of video data for events of interest in the archived video data. Examples of events of interest can include individuals entering a secure entryway or the theft of equipment or goods. Other uses are monitoring buildings and rooms within buildings to enable energy savings when building spaces are not being utilized. 
         [0003]    Current video analytic systems have the capability to track people and other objects such as cars within scenes over time. One type of metadata that would be helpful for operators is knowing when individuals enter certain parts of a scene. A number of solutions have been proposed to provide such metadata by utilizing the notion of a virtual tripwire. Individuals in the video feed are monitored to determine whether they have crossed the tripwire or not. Specific crossing times are noted, along with direction of travel. 
         [0004]    The specific technique for determining whether or not tripwires have been crossed relies on an assumption that the tripwires that are created on the screen are assumed to be at ground level in the displayed two-dimensional image. The top and bottom portions of foreground objects are then monitored to determine whether or not the bottoms of the objects overlap with the tripwire. When this occurs, these tripwire events are recorded. 
         [0005]    Such video analytic systems provide capabilities such as logging when people or other objects cross the tripwire. Often such systems will maintain statistics concerning the probability the individual in fact crossed the tripwire, and the systems may even take and send a snapshot of the video when such an event is detected. 
         [0006]    Other video analytic systems have been proposed for specifically monitoring doorways. Individuals are tracked as to how they interact with a zone around a door. Loitering around the door is noted as possible security breaches, depending on how alerts are configured. Often, these systems are concerned with detecting “tailgating” in which a second person tries to pass through a secure zone by walking with another person. The proposed systems monitor the serial actions of individuals then make conclusions concerning the individuals&#39; actions. 
       SUMMARY OF THE INVENTION 
       [0007]    The present invention concerns a video analytics system that can detect subjects when they are entering and/or exiting from an area being monitored, such as a room. The system enables a user to define a portal, such as doorway of the area being monitored, e.g., room. The system then monitors the movement of foreground objects in the area being monitored. Objects that appear only in the portal are classified as passing by the portal, e.g., doorway. Objects that initially appear in the portal and then are detected moving within the area being monitored are classified as having entered the area being monitored via the portal. Objects that are in the area being monitored and then disappear within the portal are classified as having exited the area being monitored via the portal. The system further has provisions for generating real-time alerts and performing forensic searches. 
         [0008]    In operation, the portal is defined and a video analysis system initially determines the background of a camera view and then proceeds to monitor foreground objects. It determines whether foreground objects have entered or exited the area being monitored and then generates alerts to the user. Alternatively, system can provide the user with a forensic search capability in which the portal is defined and then previously captured video is searched to determine whether or not foreground objects had previously entered or exited the area being monitored via the portal. 
         [0009]    In general, according to one aspect, the invention features a method for monitoring a portal in a video analytic system. The method comprises enabling definition of an area of a background model as a portal, and then monitoring movement of foreground objects against the background model. Foreground objects that first appear within the portal and then move out of the portal are classified as entering the area being monitored via the portal. 
         [0010]    In embodiments, the system enables the definition of the portal by an operator using a graphical user interface to identify portal within the scene. 
         [0011]    Alternatively, the definition of the portal is performed automatically by the system by analyzing video data to identify portals through which foreground objects enter and exit area being monitored. 
         [0012]    Generally, movement of the foreground objects is tracked between frames of the video data and vectors generated indicating movement of the foreground objects. This information is preferably stored as metadata. 
         [0013]    When the foreground objects first appear within the portal and then disappear within the portal, they are classified as passing by the portal. Corresponding metadata are generated but the objects are not classified as having entered the area being monitored unless they appear in the scene but outside the area of the portal. 
         [0014]    In general, according to another aspect, the invention features method for monitoring a portal in a video analytic system. This method also comprises enabling definition of an area of a background model as a portal and then monitoring movement of foreground objects against the background model. Foreground objects are classified as exiting the area being monitored when they pass into the portal and then disappear within the portal. 
         [0015]    In general, according to another aspect, the invention can be characterized as a video monitoring and analytic system. This system comprises at least one video camera generating video data of a scene and a video data and metadata archive that stores the video data from the at least one video camera. 
         [0016]    A video analysis system analyzes video data from the at least one video camera. It provides for the definition of an area of a background model of the scene of an area being monitored as a portal and then monitors movement of foreground objects against the background model. Metadata are generated and stored with the video data indicating that the foreground objects have entered the area being monitored when the foreground objects first appear within the portal and then move out of the portal and remain in the scene. 
         [0017]    In another example, the video analysis system generates metadata that the foreground objects have exited the area being monitored when the foreground objects pass into the portal and then disappear while in the portal. 
         [0018]    The system then further provides for real-time alerts and forensic searches. In more detail, with the metadata stored in the video data and metadata archive, real-time alerts can be generated based upon or triggered by individuals entering or leaving the area being monitored, such as a room being monitored by the system. Further, with the metadata stored in the archive, the system enables later forensic searches to identify when individuals entered or left the area being monitored. Further, the corresponding video data are accessed to allow the operator to identify those individuals and possibly take further action. 
         [0019]    The above and other features of the invention including various novel details of construction and combinations of parts, and other advantages, will now be more particularly described with reference to the accompanying drawings and pointed out in the claims. It will be understood that the particular method and device embodying the invention are shown by way of illustration and not as a limitation of the invention. The principles and features of this invention may be employed in various and numerous embodiments without departing from the scope of the invention. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0020]    In the accompanying drawings, reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale; emphasis has instead been placed upon illustrating the principles of the invention. Of the drawings: 
           [0021]      FIG. 1A  is a block diagram showing a network video analytic system to which the present invention relates; 
           [0022]      FIG. 1B  is a block diagram showing a network video analytic system with another architecture to which the present invention also relates; 
           [0023]      FIG. 1C  is a block diagram showing a network video analytic system with still another architecture to which the present invention also relates; 
           [0024]      FIG. 2  is a flow diagram illustrating the processing of video data to enable the tracking of objects within the scene captured in the video data; 
           [0025]      FIG. 3  flow diagram illustrating the monitoring of a portal and the generation of metadata when objects enter an area of interest via the portal; 
           [0026]      FIGS. 4A ,  4 B, and  4 C are schematic diagrams showing how the system tracks foreground objects and analyzes their movement to conclude whether the foreground objects have entered a area of interest, such as a room; 
           [0027]      FIG. 5  flow diagram illustrating the monitoring of a portal and the generation of metadata when objects exit the area of interest via the portal; 
           [0028]      FIGS. 6A and 6B  are schematic diagrams showing how the system tracks foreground objects and analyzes their movement to conclude whether the foreground object have exited the area of interest, such as a room; 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       [0029]      FIGS. 1A , B, and C illustrate the architecture of video recording and analysis systems  100  to which the present invention is relevant. 
         [0030]    The video recording and analysis system  100  generally comprises a number of video cameras  110 . The current system can handle and process the video data feeds from up to 128 separate video cameras  110 . 
         [0031]    These video cameras  110  are connected to a network video recorder  112  via a network  104 . The network  104  can be a private network, such as the local area network provided in a building. In other embodiments, the network  104  can include a combination of private networks and even public networks so that the video feeds from the video cameras  110  can be transmitted to the network video recorder system  112  enabling the recording and analysis of video in remote locations. 
         [0032]    The network video recorder  112  stores the video data and any metadata in a video data and metadata archive  114 . Generally, this data archive  114  includes both a video store  116  that includes the raw video data from the cameras  110  and a metadata store  118  that includes metadata that are generated for the associated video. 
         [0033]    The video data generated by the cameras  110  is analyzed by an analysis system  115 . This analysis system generates the metadata from the video data, which metadata are stored in the metadata store  118  in the video data and metadata archive  114 . 
         [0034]    There are a number of different places that the analysis system  115  can be deployed in the video recording and analysis system  100 . These different approaches are illustrated in each of the  FIGS. 1A ,  1 B, and  1 C. 
         [0035]    As illustrated in  FIG. 1A , in one example, the analysis system  115  is implemented with the network video recorder  112 . In this example, the video data from the video cameras  110  are received over the network  104  at the video recorder  112 . The analysis system  115  can be either a process that runs on the network video recorder  112  or a separate system that is deployed on an interface to the network video recorder system  112 . 
         [0036]    As illustrated in  FIG. 1B , in another example, the analysis system  115  is part of each of separate video cameras  110 . Here, the metadata are generated locally at each of the video cameras  110 . The video data with the metadata are then transmitted over the network  104  to the network video recorder  112 , which then stores the video data and the metadata in the archive  114 . 
         [0037]      FIG. 1C  illustrates still another example. Here, the analysis system  115  is a separate system that processes video data preferably by accessing the video data in the video data and metadata archive  114 . In this example, the video data generated by each of the cameras  110  is stored to the archive  114  by the network video recorder  112 . Then, the analysis system  115  accesses that video data from the archive  114  generates the metadata and then stores the metadata back into the metadata store  118  of the archive  114 . 
         [0038]    In some examples, all of the video data generated by the video cameras  110  are stored in the video store  116 . In other examples, video data are only stored when the analysis system  115 , upon analyzing that video, decides that an event of interest, for example, has occurred and that the video should then be stored. 
         [0039]    The video data stored in the video store  116  and the associated metadata stored in the metadata store  118  is correlated to each other via timestamps stored with the metadata that correlate to the video data from the particular video camera  110  and the event detected in the video data that gave rise to the metadata  118 . 
         [0040]      FIG. 2  illustrates how the video data from each of the video cameras  110  is analyzed by the analysis system  115 . 
         [0041]    The network video analysis system  112  first analyzes the video data from each of the video cameras  110  to generate a background model for the scenes that are being observed by each of the video cameras of the respective areas being monitored. The background model represents the stationary portion of the scene that is being monitored by each video camera. This is the portion of the scene that does not change from frame to frame. Each pixel in the captured scene is averaged over time in order to generate an image of the scene, for example a room, that contains only the portions of the scene that do not change or change only very slowly. 
         [0042]    With respect to the generated background model, any portals, for example doorways, in the scene from the area being monitored are identified in step  212 . 
         [0043]    In one implementation, portals are identified by an operator in step  214  that observes the scene and then using a graphical user interface of the analysis system  115  identifies a two dimensional area within the scene&#39;s two dimensional image that represents a portal. This is an area of the scene through which objects, such as individuals, can enter and leave the area being monitored, such as a room. 
         [0044]    In another implementation, the analysis system  115  automatically identifies these portals in step  216 . This is accomplished by analyzing the video data from the scene over time to determine regions in the scene that represent portals through which individuals or other objects can enter or leave the area being monitored. 
         [0045]    Next, during operation, the analysis system  115  monitors successive frames of the video data from each of the cameras to identify foreground objects. Foreground objects are identified by comparing the current video data to the background model. Generally, foreground objects are regions of the scene where it differs from the background model, although in some instances, if these foreground objects are stationary for long periods in the video data, then they will become part of the background model and lose their status as a foreground object. 
         [0046]    The video data are then analyzed between successive frames in step  220 . This is performed in order to track how the foreground objects move within the scene. In this way, individuals can be tracked over time as they move within the scene. Preferably, the foreground objects are identified with bounding boxes and stored as metadata as they move within a scene. Vectors characterizing the movement of the foreground objects through the scene are preferably further stored with the video data as metadata. 
         [0047]    In step  222 , these foreground objects are further tracked relative to portals, such as doorways, in the area being monitored. In this way, their interaction with portals is used to determine whether or not the foreground objects, e.g., individuals, have entered or left the area being monitored, for example. Based on this analysis, in step  224 , metadata is further stored with the video data as these foreground objects enter and leave the scene so that their movements are recorded and are accessed for later analysis. In other examples, alerts are generated in real time so that alerts can be generated to security personnel, for example, in the case where an unauthorized individual has entered a secure area being monitored by the video cameras  110 . 
         [0048]      FIG. 3  illustrates how the analysis system  115  determines that a foreground object, such as an individual, has entered the scene being monitored, a room for example. 
         [0049]    In step  310 , the analysis system  115  monitors the scene and specifically the video data from the video cameras  110  for foreground objects that first appear in the portal, such as a doorway. 
         [0050]    This entry determination is illustrated with respect to  FIG. 4A . In this example, the scene  512  is contained in the video data from a video camera  110  that is located in an area being monitored, such as a room. Within the scene, a portal or doorway  516  is defined. In the illustrated example, this portal  516  is a generally rectangle or other two dimensional area of the scene  512 . An individual  510  constitutes a foreground object. The analysis system  115  tracks this individual by drawing a bounding box  514  around the individual. In this example, the foreground object box  514  has first appeared within the area of the portal  516 . 
         [0051]    Returning to  FIG. 3 , in step  312 , the analysis system  115  monitors the foreground object  514  to determine whether or not it has stayed within the portal  516 . So long as it stays entirely within the portal, the analysis system  115  continues to track the foreground object  514  but does not conclude that it has entered the area being monitored, such as the room. 
         [0052]    In step  314 , it also checks to determine whether the foreground object  514  appears in the scene but outside the portal  516 . 
         [0053]    If it has disappeared within the portal, then it is likely that the foreground object  514  has simply passed by the doorway and has not entered the area being monitored. 
         [0054]    This scenario is illustrated in  FIG. 4B . In this example, the foreground object  514  in one frame moves to position  514 ′ in a subsequent frame. The analysis system  115  monitors the movement of this foreground object  514  between these frames and generates a vector  520  that represents the movement of the foreground object  514 , in this example the individual  510 , between successive frames and over time. If the individual continues on this path indicated by vector  520 , then it will pass out of the scene, but did not enter the area being monitored. In this way, the metadata generated by the analysis system  115  and stored in the metadata store  118  in association with this video data will simply be that an individual passed by portal  516 . 
         [0055]    Said another way, so long as no part of the foreground object  514  is ever outside the portal, the analysis system concludes that the foreground object has merely passed-by the portal and has not entered the area being monitored. This situation is illustrated in  FIG. 4C  showing the foreground object  514 ″continuing to pass by the portal  516  such that part of the individual&#39;s body  510  is obscured by the portal  516 , on one hand, yet no part of the individual&#39;s body  510  is ever outside the area if portal  516 , on the other. 
         [0056]    Returning to  FIG. 3 , when the analysis system  115  concludes that the foreground object  514  has disappeared but disappeared within the portal  516 , then the analysis system  115  generates metadata for this video data that a foreground object has passed by the portal in step  320 . 
         [0057]    The analysis system  115  also monitors whether the foreground object  514  appears within the scene but outside the portal. This scenario is illustrated in  FIG. 4A . In an earlier frame, the individual is identified as foreground object  514 . But then in a later frame, the foreground object is determined to be at position  514 ′. The analysis system  115  generates a vector  520  and stores this vector with the metadata of the video data to represent the movement of the foreground object  514  between successive frames and thus its movement over time. 
         [0058]    This movement, in the illustrated example, is further recorded as a foreground object  514  or individual  510  as entering the area being monitored. In this instance, the foreground object is first identified within the portal  516  and then located in this scene but outside the portal at a later point in time, see  514 ′ of  FIG. 4A . It is thus determined to have entered the monitored area via this portal. 
         [0059]    As a general rule, if any part of the foreground object  514  is ever outside the portal, then the object is classified has having entered the area being monitored. As result, the situation of an object moving directly towards the camera is addressed since while some part of the object is within the portal, at least part of it is outside the portal at some point. 
         [0060]    Returning to  FIG. 3 , in this instance, the analysis system  115  generates metadata in conjunction with the video data in step  316  to note that the foreground object  514  has entered the room. 
         [0061]      FIG. 5  illustrates the process by which the analysis system  115  concludes that a foreground object, such as an individual, has left the area of interest, such as a room  512 . 
         [0062]    In step  410 , the analysis system  115  tracks foreground objects  514  that are within the room  512  but not entirely within the portal  516 . These foreground objects are recorded and metadata are stored to the video data and metadata archive  114  to indicate that the foreground objects, such as individuals, are currently within the room  512  in step  412 . As the foreground objects move within the room, their position is tracked and corresponding vectors indicating their movement are stored as metadata to the metadata store  118 . 
         [0063]    This is illustrated in  FIG. 6A . Here, the foreground object moves from position  514  to  514 ′ to  514 ″ over time. The vector  520  is generated and stored as metadata to indicate the foreground object&#39;s movement within the room  512 . 
         [0064]    Returning to  FIG. 5 , the foreground objects are also monitored to determine whether they enter the area of the portal  516 , in step  414 . When the analysis system  115  determines that they have entered the portal in step  414 , the analysis system  115  then monitors the foreground objects to determine whether they stay within the portal  516 , or not, in step  418 . 
         [0065]    If they merely pass by the portal, as illustrated in  FIG. 6A , then the objects  514  are determined to have entered the area of the portal, see  514 ′, then to have left the area of the portal, see  514 ″, but are still detected within the scene as determined in step  420 . In this case, metadata are generated in step  416  that the foreground object passed by the portal  516 . 
         [0066]    The situation in which the foreground object disappears within the portal  516  is illustrated in  FIG. 6B . Here the foreground object  514  moves from the room to the area of the portal as indicated by position  514 ′. In this position, the foreground object  514 ′ is within the portal  516 . 
         [0067]    Returning to  FIG. 5 , if the foreground object is determined to no longer be in the portal in step  418  and has further disappeared from the scene as determined in step  420  without having exited from the portal and back into other parts of the scene, then the analysis system  115  concludes that the individual  510  has left the room  512  via the portal  516 . 
         [0068]    In step  422 , metadata are generated indicating that the foreground object  514  left the area of interest via the portal and this metadata are stored to the metadata store  118  along with the corresponding video, which is stored in the video store  116 . 
         [0069]    While this invention has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the invention encompassed by the appended claims.