Aiming at the defect of distinguishing similar motion from the traditional motion history image, a behavior recognition method combining improved motion history image and support vector machine was proposed. Firstly, the moving target of video frame was extracted and the external rectangular box of the moving target was marked out, the optical flow vector was calculated for each pixel in the rectangular region. Secondly, the gray value of each foreground pixel in the motion history image was set as the sum of the optical flow length at the pixel position and the historical gray value of a certain weight. While the gray value of each background pixel was attenuated by weight. Finally, the Hu moments were extracted from the motion history images and were sent as input to the SVM classifier for classification, thus completing human behavior recognition. The experimental results on KTH dataset show that the proposed method can meet the real time requirement and the recognition rate can reach 99%.

computer vision; motion recognition; motion history image; optical flow; Hu moments; support vector machine