W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust and Efficient Foreground Analysis for Real-Time Video Surveillance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Automated Visual Surveillance in Realistic Scenarios
IEEE MultiMedia
Robust background subtraction with foreground validation for urban traffic video
EURASIP Journal on Applied Signal Processing
Voting-based simultaneous tracking of multiple video objects
IEEE Transactions on Circuits and Systems for Video Technology
Unusual activity detection for video surveillance
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
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This paper presents a bottom-up tracking algorithm for surveillance applications where speed and reliability in the case of multiple matches and occlusions are major concerns. The algorithm is divided into four steps. First, moving objects are detected using an accurate hybrid scheme with selective Gaussian modeling. Simple object features balancing speed, reliability, and complexity are then extracted. Objects are matched based on their spatial proximity and feature similarity. Finally, correspondence voting solves multiple match conflicts, segmentation errors, and occlusion cases. This approach is very simple, which makes it suitable for implementation at smart surveillance visual sensing nodes. Moreover, the simulation results demonstrate its robustness in detecting occlusions and correcting segmentation errors without any prior knowledge about the objects models or constraints on the direction of their motion.