A Filter Module Used in Pedestrian Detection System

Most pedestrian detection systems are built based on computer vision technology and usually are composed of two basic modules: object detection module, and recognition module. This paper presents an efficient filtering module, which works between the two basic modules, based on extracting the 3-dimensional information from single frame images. The filter module removes the noisy objects extracted by object detection module and thus reduces the burden of the recognition module. 3-D information, such as height, width and distance are extracted from single frame images. Using this information, a Bayesian classifier is employed to implement the filter. The main contribution of this filter module is that it removed about 30% noisy objects detected by the object detection module. The total computing cost and error detection rate is reduced when this filter module is used in the pedestrian detection system.