Detection of People in Images - Neural Networks, 1999. IJCNN '99. International Joint Conference on

The paper describes a scheme for detecting and tracking people in images. The method effectively combines statistical information about the class of people with motion information for classification and tracking. In this scheme, the u n h o w n distribution of the rmages of people is approximately modeled by learning higher order statistics (HOS) information of the ‘people class’ from sample images. Given a test image, statistical information about the background is learnt dynamically. A motion detector identifies regions of activity in the image sequence. A classifier based on an HOS-based closeness measure then determines which of the moving objects actually correspond to people in motion. The tracking module uses position infonnation and an HOS-based difference measurement vector t o establish correspondence. When tested on real video data with a cluttered background, the performance of the method is found to be quite good. The method can also detect people in static imagery.