A review of statistical data association for motion correspondence
International Journal of Computer Vision
W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Data Association Methods for Tracking Complex Visual Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Multiple Humans in Complex Situations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shadow Detection by Combined Photometric Invariants for Improved Foreground Segmentation
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Content-based event retrieval using semantic scene interpretation for automated traffic surveillance
IEEE Transactions on Intelligent Transportation Systems
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This paper presents a robust approach to track multiple objects for low resolution, far-field visual surveillance applications. Multiple moving objects are detected by utilizing an adaptive background model and tracked by resolving the correspondence between their trajectory segments using proximity and appearance similarity measures. A new confidence measure is assigned to each possible match between objects and this information is maintained by a graph structure. This graph is utilized to prune and refine the trajectories. Kalman filter is used to handle discontinuities and occlusions. Proposed approach handles problems such as spurious objects, fragmentation, shadow, clutter and occlusions.