Abstract:
A surveillance system allows a user to characterize the user&#39;s environment and/or the user&#39;s surveillance application, via a selection from among a variety of predefined environments and/or applications. Preferably, the selection is from among a variety of scene configurations, such as the expected number and type of targets in a typical scene, the lighting conditions of the scene, and so on. The selected environments and/or applications effect a determination of the parameters that are used in the various algorithms and processing modules within the surveillance system. Because the selection is preferably from a variety of common scene configurations, no technical skills are required to effect an optimization of the performance of the surveillance system for a particular environment.

Description:
This application claims the benefit of U.S. Provisional Patent Application 60/446,574, filed 10 Feb. 2003. 

   BACKGROUND OF THE INVENTION 
   1. Field of the Invention 
   This invention relates to the field of surveillance systems, and in particular to a system and method for optimizing the performance of a surveillance system based on a user&#39;s characterization of the environment in which the surveillance system is deployed. 
   2. Description of Related Art 
   Surveillance systems are commonly used to automatically detect particular incidents of interest, such as the appearance of an intruder, an abandoned package, a particular face, and so on. Other applications include traffic monitoring, people counting, target tracking, and the like. 
   Different surveillance systems exhibit different performance efficiencies for different tasks and/or in different environments. Certain systems may exhibit better performance in daylight, while others may exhibit better performance in artificial light; certain systems may exhibit better performance counting people at a distance, while others may exhibit better performance with close-up images; and so on. Different algorithms or video processing functions exhibit different sensitivities to environmental factors, and different environmental conditions introduce different requirements and constraints on the processing functions and algorithms. 
   To choose an appropriate system, a user might evaluate each of a variety of systems, and select the system that performed most effectively for the user&#39;s desired objectives in the user&#39;s environment. This is usually impractical, because of the cost and effort required to install a surveillance system at a user&#39;s environment, and because of the time and effort required to simulate the scenarios for which the system is intended. Alternatively, vendors of surveillance systems promote their products, promising effective performance and continued support, and the user selects the vendor and system that appears most promising. When the vendor&#39;s system is installed, the vendor attempts to optimize the performance of the system, by adjusting the various parameters that affect the operation of the various functional units in the system to best achieve the user&#39;s objectives within the user&#39;s environment. That is, to achieve optimal performance, each installed system must typically be custom-designed for the user&#39;s objectives and environment. 
   The custom-design approach, however, generally consumes the time and effort of skilled personnel, and research efforts continue to address techniques to automate the optimization task, and/or to develop systems that are less dependent upon the particular surveillance application and/or the particular user environment. 
   BRIEF SUMMARY OF THE INVENTION 
   It is an object of this invention to provide a system and method that facilitates the performance optimization of a surveillance system for a given environment. It is a further object of this invention to provide a system and method that facilitates the performance optimization of a surveillance system for a given surveillance application. It is a further object of this invention to provide a system and method that facilitates the optimization of a surveillance system without requiring skilled resources. 
   These objects and others are achieved by providing a system that allows a user to characterize the user&#39;s environment and/or the user&#39;s surveillance application, via a selection from among a variety of predefined environments and/or applications. Preferably, the selection is from among a variety of scene configurations, such as the expected number and type of targets in a typical scene, the lighting conditions of the scene, and so on. The selected environments and/or applications effect a determination of the parameters that are used in the various algorithms and processing modules within the surveillance system. Because the selection is preferably from a variety of common scene configurations, no technical skills are required to effect an optimization of the performance of the surveillance system for a particular environment. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The invention is explained in further detail, and by way of example, with reference to the accompanying drawings wherein: 
       FIG. 1  illustrates an example block diagram of a video processing system in accordance with this invention. 
       FIG. 2  illustrates an example parameter matrix that facilitates configuring a video processing system in accordance with this invention. 
   

   Throughout the drawings, the same reference numerals indicate similar or corresponding features or functions. 
   DETAILED DESCRIPTION OF THE INVENTION 
     FIG. 1  illustrates an example block diagram of a video processing system  100  in accordance with this invention. A video input device  110  provides a sequence of images to one or more function modules  120  for processing. These modules  120  provide the result of the processing to a consolidating process  140 , and/or to other modules  120 . The consolidating process  140  processes the outputs of the modules  120  and provides reports to a user, typically via a display device  150 . For example, one of the function modules  120  may be a tracking module that tracks a person from one frame of the sequence of images to the next. Another function module  120  may be another tracking module that tracks moving objects. Another module  120 , or the process  140 , may process the outputs from these people-detecting and object-detecting modules  120  to identify abandoned objects, i.e. an object whose travel path matches a person&#39;s travel path for only an initial part of the person&#39;s travel path. If an abandoned object is detected, the process  140  communicates a notice to the user, as a message on a display device  150 . Additional processing could also be provided based upon whether the object remains stationary (abandoned object), or exhibits a different path or velocity (thrown object). Optionally, the process  140  may also initiate other notification procedures, such as sounding an alarm, communicating a message to other monitors or law-enforcement authorities, and so on. 
   As is common in the art, each function module  120  uses one or more parameters to control its performance. For example, a person-detection or object-detection module  120  searches the image for a collection of contiguous picture elements (pixels) having certain characteristics that remain contiguous from frame to frame. To distinguish a collection of pixels corresponding to a person from among other collections of pixels, the person-detection module  120  includes particular criteria, such as an expected minimum size of the collection, a particular aspect ratio, a maximum rate of movement between frames, and so on. In like manner, a face-recognition module  120  will typically include flesh-tone-color criteria, eye/nose/mouth recognition and separation criteria, etc. Other modules  120  will use similar ‘generic’ criteria, such as size and shape criteria, as well as ‘specific’ criteria, such as flesh-tone-color criteria for persons, edge-sharpness criteria for objects, and so on. 
   Because a video image is a two-dimensional representation of a three-dimensional scene, many of the aforementioned criteria will be dependent upon the location and orientation of the video input source  110  relative to the image field. The two-dimensional size and shape of a projection of a person, for example, will differ based on whether the image source  110  is at eye-level or overhead. In like manner, the size of the projection will depend upon the distance between the source  110  and the actual object, as well as the zoom-factor and other settings of the source  110 . 
   In a conventional surveillance system, as in this invention, the criteria used in each module  120  are “parameterized”, wherein the module  120  references the criteria by name, rather than value. That is, the program or circuitry that effects a comparison to determine whether the criteria is reached will use a name of a parameter, such as “Person_Minimum_Size”, and the contents of a register or other element that is referenced by this name is used for the comparison. In a conventional surveillance system, the setting of the value for each named parameter is performed by a skilled person, who takes into account the placement and orientation of the source  110  relative to a target scene, and other factors, to determine each parameter. Typically, each numeric parameter is specified as an average value and a variance about this value. 
   In addition to the use of parameters for decision-making, parameters are also used to control the operation of some modules  120 . For example, in a typical person-tracking module  120 , the module determines the new location of the tracked person by searching each next frame for a similar-appearing collection of pixels representing the person. To improve efficiency and/or to minimize the likelihood of mis-identifying another person as the tracked person, a person-tracking module typically limits its search for the similar-appearing collection of pixels to within a certain range of the location of the tracked-person identified in the prior image frame. Again, the distance between the source  110  and the actual tracked-object, as well as the zoom-factor and other settings of the source  110  will determine the limits of this search, based on the expected speed of the tracked-object in terms of pixels per frame. For example, in a long-distance view of a tracked-person, the maximum image-based movement of the tracked-person will be substantially smaller than the maximum image-based movement of the tracked-person in a near-distance view. A module  120  that receives images from a camera  110  that is situated to provide a long-distance view may limit its search to within, for example, fifty pixels of the prior location, whereas a module  120  that receives images from a camera  110  that is situated to provide a near-distance view may limit its search to within a few hundred pixels of the prior location. Typically, a skilled person sets the control parameters, such as search limits, of each module, taking into account the placement and orientation of the source  110  relative to a target scene, and other factors, to determine each control parameter. 
   Other parameters, such as parameters that define ranges of color and texture, parameters that define particular structures, orientations, and postures, and parameters that determine appropriate settings for contrast and other video controls, are common in the art and typically require the efforts of a skilled person to customize/optimize the modules  120  and processes  140  for a particular environment. In like manner, the choice of which modules  120  or processes  140 , or which elements of modules  120  or processes  140  to use in a given environment may also be parameterized. For example, the system  100  may include a variety of motion analysis models, and the customization/optimization of the system  100  may include the selection of one or more of these models for use in the user&#39;s particular environment, or for the user&#39;s particular surveillance objectives. 
   In accordance with this invention, the surveillance system  100  includes a database  170  of potential characterizations C 1 -Cn of a user environment and/or a user&#39;s surveillance objectives. The term “database” is used herein in general terms, meaning a collection of information that is organized for use based on one or more selections from among the collection. The database  170  may be organized as a discrete entity that is internal or external to the physical devices forming the modules  120 ,  140 , or its information may be distributed among a variety of entities, including the modules  120 ,  140 . 
   Using a selector  160 , a user selects from among the variety of characterizations C 1 -Cn in the database  170  to identify the characterizations  175  that correspond to the user&#39;s environment and/or objectives. For example, the characterizations C 1 -Cn may include such characterizations as: “1-3 people”, “4-8 people”, . . . “no vehicles”, “1-2 vehicles”, . . . “indoor”, “outdoor”, . . . “day”, “night”, . . . “full image”, “overhead image”, . . . and so on. From these selected characterizations  175 , a configuration module  180  provides the appropriate parameters to the modules  120 ,  140 . 
   In an example embodiment, the selector  160  is configured to display icons that present typical characteristic images, or scene configurations, such as: a scene that shows three people, each person-image occupying about a quarter of the image scene; a scene that shows a half dozen people, each person-image occupying about an eight of the image scene; a scene that shows a dozen people; and so on. Other icons may show these groupings of people in images taken from different camera angles. Other icons may show an outdoor scene, a brightly lighted scene, a dimly lighted scene, a night scene, a snow scene, and so on. A user views a scene from the actual video source  110  in the user&#39;s environment, and then selects the one or more icons that appear similar to the scene from the source  110 . Alternatively, the selector  160  may be configured to display a list of descriptive characterizations, and the user selects one or more items in the list that describe the scene from the source  110 . Other methods for facilitating the selection of characterizations from among a variety of characterizations will be evident to one of ordinary skill in the art in view of this disclosure. 
     FIG. 2  illustrates an example parameter matrix  200  that facilitates configuring a video processing system in accordance with this invention. The first column  280  represents the user&#39;s input, as determined from the selector  160  of  FIG. 1 . The second column  281  represents the items for which parameters are provided, including, for example, “people”, “objects”, “automobiles”, and so on. The remaining columns  282  correspond to the aforementioned parameters for the function modules  120  in  FIG. 1 , such as the “size” of the item, the “shape” or “aspect ratio” of the item, the “speed of movement” of the item, and so on. Two numeric entries are illustrated in each column entry, to illustrate that sets of values, such as an average and variance, or a minimum and maximum, or an initial value and an increment, etc., may be associated with each parameter. 
   The first set of rows indicate the default parameters that are used for “people”, “objects”, and “automobiles” if the user does not provide an input regarding the particular item. These default parameters are based on an assumed typical surveillance scene, such as a scene comprising about a half dozen people, or a half-dozen automobiles, consistent with the default parameters of typical conventional surveillance systems. For example, if the user does not select a characterization related to the number of people that may occupy the image, the entry  201  indicates a size parameter of ( 160 ,  80 ). In this example, the first element of the set of the size parameter corresponds to an average, and the second element corresponds to a variation about this average. The units of the size measurement in a preferred embodiment are ‘normalized’ pixels, based on a normalized resolution of the video source  110 , such as a resolution of 1024×1024 pixels. Thus, the entry  201  indicates in this example that the default average size of a person-image will be 160 pixels, with a variation of 80 pixels about this average. In like manner, the average size of an object is initially assumed to be 50 pixels, with a variation of 30 pixels, and the average size of an automobile is initially assumed to be 180 pixels, with a variation of 70 pixels. In this example, the “size” parameter corresponds to the number of units in the vertical direction, although other size measures, such as the total number of pixels (i.e. an area measure) may also be used. 
   If the user characterizes the images from the source  110  as having a field of view that only accommodates a few (1-3) people, each person-image will be fairly large, as indicated by the entry  211 , which indicates that the average size of a person will be 320 units, compared to the default entry  201 , which indicates that the average size of a person will be 160 units. In like manner, if the user characterizes the source  110  as providing images that accommodate more than ten people, the entry  221  indicates that the average size of each person-image will be 60 units. Note that the “1-3 people”, or “&gt;10 people” characterization of the user&#39;s source  110  also affects the average size of an “object”, at entries  212  and  222 , which indicate that the average size of an object will be 100 units and 30 units, respectively, compared to the default entry  202 , which indicates that the average size of an object will be 50 units. 
   As mentioned above, the units and values of each parameter is dependent upon the particular module  120  that uses the parameter. For example, at entry  208 , the “aspect ratio” parameter of an object is given as “1, 1”. In this example, the module  120  that uses the aspect ratio parameter may be configured to interpret a “1, 1” parameter as signifying that the item can have any aspect ratio. In like manner, the sign of a parameter may be used to communicate a flag that is independent of the actual numeric value of the parameter. That is, a module  120  may use the sign of the parameter to effect one of two possible tests, and may use the absolute value of the parameter in another section of code, independent of its sign. Similarly, a zero value of a parameter may signal the module  120  to use a default value for the particular parameter, or to bypass a particular test, and so on. Such techniques of communicating control signals to a function module via an encoding of a parameter&#39;s value, as well as other encoding techniques, are common in the art of programming. 
   The multiple planes  250 ,  251 ,  252  of the matrix in  FIG. 2  illustrate that other user characterizations of the images from the source  110  may introduce different sets of parameter values. For example, the size and aspect ratio of an item can be expected to differ depending upon whether the camera is at eye-level or overhead. In like manner, parameters that relate to luminance or color values may differ depending upon whether the scenes are indoor or outdoor, the time of day, and other user-selectable characterizations. The characterizations of the images from the source  110  may also be arranged in a hierarchy, or other ordering, that facilitates selection. For example, if the scene is characterized as being an outdoor scene, additional characteristics, such as the season of the year, the current weather conditions, and so on, may also be solicited and selected. If the scene is characterized as being an indoor scene, additional characteristics, such as a choice among an office scene, a factory scene, a home scene, and so on, may be solicited and selected, followed perhaps by a characterization of the normal hours of business operation, routine home schedules, and so on. 
   Of particular note, using the principles of this invention, a user is able to customize the performance of the surveillance system  100  of  FIG. 1  without direct knowledge of the particular parameters that are used by each module  120 ,  140  in the surveillance system. The pre-configured characterizations C 1 -Cn in the database  170  include the ‘mapping’ between a user-selected characterization  280  and the detailed parameters  201 - 222  required by each module  120 ,  140 , as illustrated in the example matrix  200  of  FIG. 2 . 
   The foregoing merely illustrates the principles of the invention. It will thus be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the invention and are thus within its spirit and scope. For example, the configuration process is described above as an integral part of the surveillance system. One of ordinary skill in the art will recognize that the determination of surveillance system parameters from scene characterizations can be effected independent of the surveillance process, and the determined parameters can be communicated to the surveillance system for subsequent use. By separating the configuration process from the surveillance process, the configuration process of this invention can be used to augment or enhance existing surveillance systems. In like manner, although the user of the surveillance system is presented above as the intended user of the configuration process of this invention, one of ordinary skill in the art will recognize that the configuration process can be used, for example, by the vendor or manufacturer of the surveillance system to pre-configure the surveillance system, and the conventional custom-configuration at the user&#39;s site can be performed to adjust these pre-configured parameters for a particular user or particular environment. These and other system configuration and optimization features will be evident to one of ordinary skill in the art in view of this disclosure, and are included within the scope of the following claims.