Study On Abnormal Behavior Detection And Recognition Based On Video Image

Human action recognition is to analyze and recognize the pattern of human motion, which is a wide interesting research topic in computer vision field. Abnormal behaviors recognize is an important component of human motion analysis and there are five kinds of abnormal behavior, including jump, run, bend, jack, wave in this paper. Research in this domain involves wide application in the intelligent security monitoring system, which not only completes the safeguard tasks efficiently, but also saves a great deal of human labor.Two algorithms of moving object detection are described in this paper, which are the instantaneous difference method and background modeling method. Their advantages and disadvantages are analyzed. An approach of combining the background subtraction and temporal difference is proposed to resolve the problem that current methods can't obtain the integrated moving human contour. Two consecutive frames are subtracted to pick up background pixel in this method, and then the Gaussian model of every pixel is built. Background subtraction is used to detect moving objects.Next, the shade processing is discussed. The shade detection algorithms in RGB and HSV color space are introduced. An improved algorithm of detecting the shade combining two color spaces is proposed to improve the accuracy and real-time performance. Meanwhile, Image post-processing including median filtering and mathematic morphology method is discussed..An improved Hu moment method is used to abnormal behavior recognition. Hausdorff distance is used to calculate the similarity between recognizing behavior feature vector and normal behavior template one and determine whether the behavior is abnormal or not. Experimental results show that proposed method can detect and recognize human abnormal behavior accurately and efficiently.