Recursive Estimation of Motion, Structure, and Focal Length
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Metric for Distributions with Applications to Image Databases
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Counting Crowded Moving Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Local empirical templates and density ratios for people counting
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Automatic scene calibration for detecting and tracking people using a single camera
Engineering Applications of Artificial Intelligence
Accurate pedestrian counting system based on local features
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
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Vision-based people counting systems have wide potential applications including video surveillance and public resources management. Most works in the literature rely on moving object detection and tracking, assuming that all moving objects are people. In this paper, we present our people counting approach based on face detection, tracking and trajectory classification. While we have used a standard face detector, we achieve face tracking combining a new scale invariant Kalman filter with kernel based tracking algorithm. From each potential face trajectory an angle histogram of neighboring points is then extracted. Finally, an Earth Mover's Distance-based K-NN classification discriminates true face trajectories from the false ones. Experimented on a video dataset of more than 160 potential people trajectories, our approach displays an accuracy rate up to 93%.