Abstract: In a digital video surveillance system, a number of processing stages are employed to identify foreground regions representing moving objects in a video sequence. An object tracking stage (5) is also provided in order to identify a correspondence between candidate objects in a current frame and those that have already been identified in one or more previous frames. In this way, it is possible to calculate the path taken by the or each foreground object and to record this path information in a trajectory database. In order to improve tracking performance, the object tracking stage (5) employs a state transitional object management scheme which determines whether or not a particular object is tracked. The path information generated by the object tracking stage (5) can be displayed on a video monitor (15) of the surveillance system and/or made available to high level applications (7).

Abstract: In a digital video surveillance system, a number of processing stages are employed to identify foreground regions representing moving objects in a video sequence. An object tracking stage 5 is also provided in order to identify a correspondence between candidate objects in a current frame and those that have already been identified in one or more previous frames. In this way, it is possible to calculate the path taken by the or each foreground object and to record this path information in a trajectory database. In order to improve tracking performance, the object tracking stage 5 employs a state transitional object management scheme which determines whether or not a particular object is to be tracked. As part of the object management scheme, spurious objects, which are identified on the basis of their motion characteristics, are deleted from the system. This ensures that valuable processing resources are not wasted tracking unwanted artifacts which may represent, for example, noise or random motion.

Abstract: The invention provides a method and apparatus for performing correspondence estimation between pixels of a stereo image pair to obtain matching information for corresponding pixels in each image. To perform a match for a particular pixel in a first image firstly an adaptive curve is constructed about the pixel, being a sequence of connected pixels with similar intensity values to the pixel being matched. The adaptive curve thus constructed is then used as a matching element within the second image to find a matching pixel representative of the same 3D scene point in the second image to the particular pixel. By performing matching in this manner for every pixel in an image, accurate disparity maps can be obtained which are then used in a known image synthesis algorithm to produce novel images of a scene of improved quality.

Abstract: In an intelligent video surveillance system, video processing software performs a number of operations on video data received from a camera, including foreground extraction, shadow removal and object tracking. The foreground extraction stage classifies each pixel of a received frame as representing either foreground or background. Since shadow regions can be wrongly classified as foreground, a two-branch shadow removal operation is employed, comprising weak shadow removal (32) and strong shadow removal (33). The purpose of weak shadow removal (32) is to remove only the most severe shadows in each frame. Conversely, the purpose of strong shadow removal (33) is to remove substantially every shadow present in each frame. By comparing the overlap of foreground regions in the two resulting images, it is possible to identify foreground regions that have fragmented due to strong shadow removal, and thereafter classify them as representing a common foreground object.

Abstract: In a digital video surveillance system, a number of processing stages are employed to identify foreground regions representing moving objects in a video sequence. An object tracking stage (5) is also provided in order to identify a correspondence between candidate objects in a current frame and those that have already been identified in one or more previous frames. In this way, it is possible to calculate the path taken by the or each foreground object and to record this path information in a trajectory database. In order to improve tracking performance, the object tracking stage (5) employs a state transitional object management scheme which determines whether or not a particular object is tracked. The path information generated by the object tracking stage (5) can be displayed on a video monitor (15) of the surveillance system and/or made available to high level applications (7).

Abstract: In an intelligent video surveillance system, video processing software performs a number of operations on video data received from a camera, including foreground extraction, shadow removal and object tracking. The foreground extraction stage classifies each pixel of a received frame as representing either foreground or background. Since shadow regions can be wrongly classified as foreground, a two-branch shadow removal operation is employed, comprising weak shadow removal (32) and strong shadow removal (33). The purpose of weak shadow removal (32) is to remove only the most severe shadows in each frame. Conversely, the purpose of strong shadow removal (33) is to remove substantially every shadow present in each frame. By comparing the overlap of foreground regions in the two resulting images, it is possible to identify foreground regions that have fragmented due to strong shadow removal, and thereafter classify them as representing a common foreground object.

Abstract: In a digital video surveillance system, a number of processing stages are employed to identify foreground regions representing moving objects in a video sequence. An object tracking stage 5 is also provided in order to identify a correspondence between candidate objects in a current frame and those that have already been identified in one or more previous frames. In this way, it is possible to calculate the path taken by the or each foreground object and to record this path information in a trajectory database. In order to improve tracking performance, the object tracking stage 5 employs a state transitional object management scheme which determines whether or not a particular object is to be tracked. As part of the object management scheme, spurious objects, which are identified on the basis of their motion characteristics, are deleted from the system. This ensures that valuable processing resources are not wasted tracking unwanted artefacts which may represent, for example, noise or random motion.

Abstract: The invention provides a method and apparatus for performing correspondence estimation between pixels of a stereo image pair to obtain matching information for corresponding pixels in each image. To perform a match for a particular pixel in a first image firstly an adaptive curve is constructed about the pixel, being a sequence of connected pixels with similar intensity values to the pixel being matched. The adaptive curve thus constructed is then used as a matching element within the second image to find a matching pixel representative of the same 3D scene point in the second image to the particular pixel. By performing matching in this manner for every pixel in an image, accurate disparity maps can be obtained which are then used in a known image synthesis algorithm to produce novel images of a scene of improved quality.