Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Multi-Pedestrian Tracking in Thermal-Visible Surveillance Videos
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
MOCUS: moving object counting using ultrasonic sensor networks
International Journal of Sensor Networks
Segmentation and Tracking of Multiple Humans in Crowded Environments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Counting People from Multiple Cameras
ICMCS '99 Proceedings of the 1999 IEEE International Conference on Multimedia Computing and Systems - Volume 02
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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.