Human upper body detection and splitting method applied to low-contrast video

Abstract

The invention relates to a human upper body detection and splitting method applied to a low-contrast video. The method mainly comprises two processes. In the first process, a communicated area representing a foreground object is extracted from a current frame by a background subtraction technology and a morphological method; and for each foreground area, the shape features of a polar-coordinate-based two-dimensional histogram corresponding to the foreground area are extracted as the input of a pre-trained support-vector-machine-based classifier, and a class tag corresponding to a human upper body class and a class tag corresponding to a non-human upper body class are output. In the second process, when an area which is identified as a human body area is misjudged as a non-human body area, the area is represented by an energy function, an inaccurate contour line is corrected by an energy function minimization process at the same time, and finally, a background frame is updated on the basis that an accurate foreground human body contour is obtained. By the method, a video with low contrast and resolution can be processed in real time, and both detection accuracy and a splitting result can meet the requirements of application.

Description

Technical field: [0001] The invention relates to the technical field of video processing, in particular to a method for detecting and extracting an upper body region of a human body, in particular to a method for detecting and segmenting the upper body of a human body suitable for low-contrast videos. Background technique: [0002] Automatic detection and segmentation of human body regions in videos are crucial steps for two different surveillance applications. Human detection methods typically find foreground objects in videos and identify them as human or non-human regions based on shape, color, and other characteristics. Background culling is a common preprocessing technique for extracting foreground regions. The other category is based on machine learning and applies many new features suitable for machine learning. Gradient-based features are most representative. These methods do not require preprocessing for background culling but come at the cost of high computation...

AI Technical Summary

Problems solved by technology

Although some improved background culling algorithms can solve the above problems, if the foreground object remains still in front of the camera for a considerable period of time, the foreground will gradually change into the background

In addition, because the quality of the CCD chip of the camera equipped with many surveillance systems is not high, so that the video contrast obtained is low, and the existing methods will be more difficult to process these videos

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