Combing spatial and temporal features for crowd counting with point supervision
Haiying Jiang, Weidong Jin, Zhibin Yu, Peizhen Xu · 2017
In this paper, we present a new approach to count the number of people that cross a counting line from video images. This paper focuses on point-level annotation in training images and incorporate spatial features along with novel temporal features in training the structured random forest for estimating crowd density. By computing the crowd velocity, we model the crowd counting map as elementwise multiplication of crowd density map and crowd velocity map. Integrating over crowd counting map on the line of interest(LOI) locations leads to the instantaneous LOI counting numbers. We show that results are comparable to those obtained when using more complex and costly techniques.