Pedestrian Tracking under Dense Crowd

Abstract

In dense scenes, a large number of individuals can cause more serious problems such as blurred vision, chaotic scenes and so on. In view of the above problems, a tracking algorithm based on human head shoulder model is proposed. Support vector machine is used to train the classifier by machine learning. The average accuracy rate of pedestrians tracking in high density scenes is about 95%.