IEEE Transactions on Pattern Analysis and Machine Intelligence
VR '03 Proceedings of the IEEE Virtual Reality 2003
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
International Journal of Computer Vision
Estimating pedestrian counts in groups
Computer Vision and Image Understanding
Evaluating multiple object tracking performance: the CLEAR MOT metrics
Journal on Image and Video Processing - Regular
Using social effects to guide tracking in complex scenes
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple target tracking in world coordinate with single, minimally calibrated camera
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
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In this paper, we consider the problem of finding and localizing social human groups in videos, which can form a basis for further analysis and monitoring of groups in general. Our approach is motivated by the collective behavior of individuals which has a fundament in sociological studies. We design a detection-based multi-target tracking framework which is capable of handling short-term occlusions and producing stable trajectories. Human groups are discovered by clustering trajectories of individuals in an agglomerative fashion. A novel similarity function related to distances between group members, robustly measures the similarity of noisy trajectories. We have evaluated our approach on several test sequences and achieved acceptable miss rates (19.4%, 29.7% and 46.7%) at reasonable false positive detections per frame (0.129, 0.813 and 0.371). The relatively high miss rates are caused by a strict evaluation procedure, whereas the visual results are quite acceptable.