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Occlusion detection and automatic adaption of a generic pedestrian detector to a specific scene are difficult problems in intelligent monitoring. When a detector trained in a specific scene is applied on a new scene, its accuracy will decrease greatly. To solve this problem, we propose a new detection algorithm in which motion regions of interest based on motion information are obtained quickly by a flash-bit computing method. Also we focus on the case in which a single target converts to be a difficult one due to multiple overlapping between pedestrians. Key points with BRISK feature which computed and saved before are used to match difficult targets in occlusions. Normed proposals which proved to have higher confidence are used to correct the location and shape of detection windows, results in a five percent increasing of detection accuracy. Results of comparative experiments of five different detectors on three motion pedestrian datasets show that proposed algorithm achieves not only a real time speed, but also the best accuracy that more than half of difficult targets are detected successfully.

Moving Pedestrian Detection Using Normed Proposals and Key Points Matching