Pedestrian Detection Under Dense Crowd
Ge Yang, Siping Chen · 2018
In dense scenes, a large number of individuals can cause more serious problems such as blurred vision, chaotic scenes, complex behaviors and so on. For low density pedestrian detection algorithm, the accuracy of detection will be greatly reduced, even detection failure when facing these problems in high density scenes. In view of the above problems, the detection algorithm based on human head shoulder model is proposed. Support vector machine is used to train the classifier by machine learning. The detection algorithm proposed in this paper achieves 94% detection by using MIT and INRIA data sets.