Pedestrian Detection with Local Feature Assistant

Until now, existing pedestrian detection systems usually use global features (e.g. appearance or motion) of human body to detect pedestrian; however, the detection rate needs to be improved in many situations since sometimes the global features can not be obtained. For example, a pedestrian may be partly covered by a car or his/her part may hide into the background. Therefore it is essential to adopt some local features of key parts of human body to assist pedestrian detection. In this paper, we propose a method using some key local features of human body to help pedestrian detection. Since the introduction of additional features will cost the system more time, in order to ensure the detection speed, we firstly use both appearance and motion global features of human body to select candidates, and then use local features of head and leg to do further confirmation. In the confirmation stage, we use three kinds of local features (head appearance, face color and hair color) to detect the head of each candidate; at the same time, we also choose some particular local appearance features to detect the leg. The experimental results indicate that this method can improve detection rate with almost the same detection speed; additionally, it can reduce false alarm sometimes.