Combining static and dynamic features for real-time moving pedestrian detection

Pedestrian detecting and tracking are critical techniques in video monitoring. However, real-time pedestrian detection is still challenging in surveillance videos with complex background. In existing frameworks, feature extractions are usually time-consuming to achieve high detection accuracy. In this paper, we propose to combine sparse static and dynamic features to improve the feature extraction speed while keeping high detection accuracy. Firstly, the static sparse feature is extracted using a fast feature pyramid in each frame. Secondly, sparse optical flow is used to extract sparse dynamic feature among successive frames. Thirdly, we combine the two types of feature in the Adaboost classification. Experiments show that the average miss rate of our approach is 17%. The detection rate is up to 22 fps in a Matlab implementation. It shows that our approach achieves optimal detection accuracy compared to the state-of-the-art real-time pedestrian detection algorithms.