International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Real-time closed-world tracking
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Hydra: Multiple People Detection and Tracking Using Silhouettes
ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Indoor and Outdoor People Detection and Shadow Suppression by Exploiting HSV Color Information
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
Segmentation and Tracking of Multiple Moving Objects for Intelligent Video Analysis
BT Technology Journal
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Human Tracking by IP PTZ Camera Control in the Context of Video Surveillance
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Fuzzy Feature-Based Upper Body Tracking with IP PTZ Camera Control
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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This paper addresses the problem of detecting and tracking multiple moving people when the scene background is not known in advance. We have proposed a new background detection technique for dynamic environment that learns and models the scene background based on K-mean clustering technique and pixel statistics. The background detection is achieved using the first frames of the scene where, the number of these frames needed depends on how dynamic is the observed environment. We have also proposed a new feature-based framework, which requires feature extraction and feature matching, for tracking moving people. We have considered color, size, blob bounding box and motion information as features of people. In our feature-based tracking system, we have used Pearson correlation coefficient for matching feature-vector with temporal templates. The occlusion problem has been solved by subblobbing. Our tracking system is fast and free from assumptions about human structure. The tracking system has been implemented using Visual C++ and OpenCV and tested on real-world videos. Experimental results suggest that our tracking system achieved good accuracy and can process videos close to real-time.