A Crowd Flow Segmentation Method based on Deep Motion Transformation Network
Xinfeng Zhang, Qili Ni, Shuhan Chen, Baoqing Yang, Bin Li · 2021
The crowd motion in public places is generally disorderly but locally orderly. Therefore, dividing the crowd flow into regions with basically consistent motion states can help us better understand and analyze the crowd's motion states. For this reason, a deep motion transformation network is proposed to segment the crowd flow into different motion states, which avoids the problem of parameter selection based on the clustering method. We test the method in different crowd density scenarios, and the experimental results show that the proposed method can achieve a better segmentation effect than the previous methods.