Crowd Counting via Head Detection and Motion Flow Estimation

Huiyuan Fu, Huadong Ma, Hongtian Xiao

Abstract: Crowd counting with heavy occlusions is highly desired for public security. However, few works have been studied towards this goal. Most previous systems only count passing people robustly without heavy occlusions, else they have to estimate the crowds to a certain extent. To solve this difficult problem, this paper proposes an effective algorithm by combining head detection and motion flow estimation together. First, we detect each head in the crowd by using our proposed scene-adaptive scheme on depth data. Then, we estimate the motion flow based on the interest points in each head region on color data. We can ultimately achieve multi-direction crowd counting results with the loop of above steps. Based on this approach, we have built a practical system for reliable crowd counting. Extensive experimental results show that our system is effective.