CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning and Classification of Complex Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Alignment by maximization of mutual information
Alignment by maximization of mutual information
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Fuzzy spatial constraints and ranked partitioned sampling approach for multiple object tracking
Computer Vision and Image Understanding
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In multiple-object tracking, the lack in prior information limits the association performance. Furthermore, to improve tracking, dynamic models are needed in order to determine the settings of the estimation algorithm. In case of complex motions, the dynamic cannot be learned and the task of tracking becomes difficult. That is why on-line spatio-temporal motion estimation is of crucial importance. In this paper, we propose a new model for multiple target online tracking: the Energetic Normalized Mutual Information Model (ENMIM). ENMIM combines two algorithms: (i) Quadtree Normalized Mutual Information, QNMI, a recursive partitioning methodology involving a region motion extraction; (ii) an energy minimization approach for data association adapted to the constraint of lack in prior information about motion and based on geometric properties. ENMIM is able to handle typical problems such as large inter-frame displacements, unlearned motions and noisy images with low contrast. The main advantage of ENMIM is its parameterless and its capacity to handle noisy multi-modal images without exploiting any pre-processing step.