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Pedestrian counting in video sequences using optical flow clustering

Existing systems for counting pedestrians in video sequences have the problem that counting accuracy is worse when many pedestrians are present and occlusion occurs frequently. In this paper, we introduce a method of clustering optical flows in video frames to improve the counting accuracy in cases where occlusion occurs. The proposed method counts the number of pedestrians by using pre-learned statistics, based on the strong correlation between the number of optical flow clusters detected by our method and the actual number of pedestrians. We evaluate the accuracy of the proposed method using several video sequences, focusing in particular on the effect of parameters for optical flow clustering. We find that the proposed method improves the counting accuracy by up to 25% as compared with a non-clustering method. We also report that using a clustering threshold of angles less than 1° is effective for enhancing counting accuracy.