Using Articulated Models for Tracking Multiple C. elegans in Physical Contact
Journal of Signal Processing Systems
Mask Particle Filter for Similar Objects Tracking
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Occlusion reasoning for tracking multiple people
IEEE Transactions on Circuits and Systems for Video Technology
Sequential particle generation for visual tracking
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Multimedia - Special issue on integration of context and content
Tracking a group of highly correlated targets
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Sequential particle filtering for conditional density propagation on graphs
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Integrated video object tracking with applications in trajectory-based event detection
Journal of Visual Communication and Image Representation
Editors Choice Article: Tracking highly correlated targets through statistical multiplexing
Image and Vision Computing
Advanced formation and delivery of traffic information in intelligent transportation systems
Expert Systems with Applications: An International Journal
Computers & Mathematics with Applications
Extended MHT algorithm for multiple object tracking
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
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This paper presents a method which avoids the common practice of using a complex joint state-space representation and performing tedious joint data association for multiple object tracking applications. Instead, we propose a distributed Bayesian formulation using multiple interactive trackers that requires much lower complexity for real-time tracking applications. When the objects' observations do not interact with each other, our approach performs as multiple independent trackers. However, when the objects' observations exhibit interaction, defined as close proximity or partial and complete occlusion, we extend the conventional Bayesian tracking framework by modeling such interaction in terms of potential functions. The proposed "magnetic-inertia" model represents the cumulative effect of virtual physical forces that objects undergo while interacting with each other. It implicitly handles the "error merge " and "object labeling" problems and thus solves the difficult object occlusion and data association problems in an innovative way. Our preliminary simulations have demonstrated that the proposed approach is far superior to other methods in both robustness and speed