Counting People in Crowded Scenes via Detection and Regression Fusion

It is particularly important for surveillance systems to track the number of people in crowded scenes. In this paper, we look into this problem of counting people in crowded scenes and propose a framework that fuses information coming from detection, tracking and region regression together. For counting by regression, we propose to use region covariance features in the form of Sigma Sets in conjunction with interest point features. Experimental results on two benchmark datasets demonstrate that using region covariance features for the purpose of people counting yields effective results. Moreover, our results indicate that fusing detection and regression is beneficial for more accurate people counting in crowded scenes.