Detection of people in images

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 unknown distribution of the images 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 information and an HOS-based difference measurement vector to 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.