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The pedestrian detection is a hot research topic in computer recognition. It involves not only the pedestrian location information but also the intrusion detection function, which has wide prospects in the application of vehicle traffic, campus monitoring, and building guard. However, the identification accuracy and recognition speed play an important role in the pedestrian detection, which calls for a fast pedestrian detection approach. The general pedestrian detection implementation, based on the integral channel features method and soft cascade classifier, is the popular technique in the current business application since its better speed and accuracy. Thus, this method uses the feature approximation technique and multiple classifiers to achieve the feature computing, which speeds up the detection without resizing image. To this end, this paper is motived to propose a multi-scale handling method for the fast pedestrian detection, using the tactics detection from sparse to dense. Our pedestrian detection method consists of four parts functions, mainly pedestrian statistics and intrusion detection, pedestrian tracking and pedestrian flow statistics. All these modules are introduced with its details about design and implementation. In Addition, the proposed multi-scale handling method can be applied into most of object detectors to improve their recognition speed. In conclusion, our proposed approach has a good potential application prospect in the video surveillance system.

A Novel Approach to Design the Fast Pedestrian Detection for Video Surveillance System