Counting People in Crowded Environments by Fusion of Shape and Motion Information

Knowing the number of people in a crowded scene is of big interest in the surveillance scene. In the past, this problem has been tackled mostly in an indirect, statistical way. This paper presents a direct, counting by detection, method based on fusing spatial information received from an adapted Histogram of Oriented Gradients-algorithm (HOG) with temporal information by exploiting distinctive motion characteristics of different human body parts. For that purpose, this paper defines a measure for uniformity of motion. Furthermore, the system performance is enhanced by validating the resulting human hypotheses by tracking and applying a coherent motion detection. The approach is illustrated with an experimental evaluation.