Counting Crowded Moving Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Viewpoint Invariant Approach for Crowd Counting
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Robust Foreground Detection In Video Using Pixel Layers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating pedestrian counts in groups
Computer Vision and Image Understanding
ETISEO, performance evaluation for video surveillance systems
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Dictionary learning based object detection and counting in traffic scenes
Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras
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Cameras are becoming a common tool for automated vision purposes due to their low cost. In an era of growing security concerns, camera surveillance systems have become not only important but also necessary. Algorithms for several tasks such as detecting abandoned objects and tracking people have already been successfully developed. While tracking people is relatively easy, counting people in groups is much more challenging. The mutual occlusions between people in a group make it difficult to provide an exact count. The aim of this work is to present a method of estimating the number of people in group scenarios. Several considerations for counting people are illustrated in this paper, and experimental results of the method are described and discussed.