Algorithm Research For Human Body Detection In Video Image

The key techniques of intelligent surveillance based on computer vision are researched in this paper. Generally this system consists 3 parts that are human body detection and behavior understanding and high leveled judgment output, among which human body detection is crucial and basis for the latter two modules, that is the point why this paper focuses on human body detection in image sequence. The whole work includes three aspects and there are some innovations, which have been proved valid through emulation experiments. They are introduced as following.(1) video image filter designAccording to the characteristics of pulse noise in video image, a neural network PCNN (Pulse Coupled Neural Network) is applied to aggregate the polluted pixels contaminated by pulse noise and the non-polluted ones, then median filter is used to smooth the contaminated pixels. Tests show this algorithm is more valid in filtering pulse noise and superior in preserving the edge and texture of image.(2) An algorithm to detect human body in video image should work well whether the human ismoving or not. Background modeling segmentation methods are capable to detect human body on the condition that the human body should keep moving, and become void when keep stable. Two improved algorithms are put forward herein.â‘ MOGs (mixture of Gaussians) are used in modeling background, which is apt for slowing changed background (e.g. illuminant slow changes and wavering leaves). But this traditional method becomes void when abrupt illuminant changing. An improved method is put forward to overcome its incapability.â‘¡Segmented human body image is not integrated for many method, which could not be sewed up through morphological techniques. A new method is proposed, which is on the basis that the foreground pixels within an adjacent area inside of a human body image are closely correlated; while the interframe background pixels in a fixed position are consistent. Tests prove it works well, which could get more integral human body image.(3)the two above methods are limited to detect human body when human body is always moving in a scene. The following algorithm is based on human skin color detection, which could compensate this limitation. After intensive analysis of method [71], a new data prediction algorithm is adopted. Tests show this algorithm is of high sensitivity and high resolution to detect human skin (e.g. human face) when illuminate keeps stable; and could segment most of skin area when illuminant changes to some extent, which prove robust against illuminant variation.