Robust crowd counting using detection flow

Crowd counting which aims at obtaining the number of people within a scene is an important computer vision task. While most previous methods try to count people within one frame, this paper addresses this problem using the detection flow which is defined as a set of object detection responses along the temporal video sequence. We argue that counting based on detection flow provides a better way to estimate the crowd size with following merits: 1) it can greatly alleviate the common weakness of an object detector including miss detection and false alarms; 2) it is robust to temporal object occlusions and noises; 3) it is more competent to give specific descriptions of the crowd, e.g. crowd moving directions and target locations. Experiment results on PETS 2009 dataset demonstrate the potential of this method.