With the rapid development of the society, public security is becoming more and more important and being concerned by the government. Passenger flow is one of the most important factor in the public security area. With the information of the passenger of the flow, the manger can reasonable schedule the resources and provide a better management. In recent years, more and more research is concentrated on people counting. The method of people-counting based on radio machine can be used for the indoor scene like shopping malls、buildings. But for the outdoor scene like the Olympic Park, it is impossible to use the traditional method. On the other hand, Computer vision can be well used for solving this problem. In this article, a method based on object detection and point tracking are proposed to solve this problem. The main contribution of this thesis includes: (1) A new feature based on LBP and EOH are proposed for pedestrian detection. When extracting the feature of the image, the pedestrian area is divided into multi-blocks at first, and then the feature is extracted separately in each block, finally all the features are contacted together to describe the image; The accurate rate are greatly improved , while importing the space information. (2) Firstly the feature points of the image are tracked, and then a model is used for converting the tracking route of the feature points to the route of the related pedestrian. (3) A multi-counting line strategy is used for counting, this strategy can reduce the dependence on fast detection of people and improve the counting accurate rate.
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