Video Tracking Association Problem Using Estimation of Distribution Algorithms in Complex Scenes
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Note: Target tracking with incomplete detection
Computer Vision and Image Understanding
A jump-diffusion particle filter for tracking grouped and fragmented objects
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Multiple hypothesis target tracking using merge and split of graph’s nodes
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
A multiple hypothesis based method for particle tracking and its extension for cell segmentation
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
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Bayesian target tracking methods consist in filtering successive measurements coming from a detector. In the presence of clutter or multiple targets, the filter must be coupled with an association procedure. Classical Bayesian multi-target tracking methods rely on the hypothesis that a target can generate at most one measurement per scan and that a measurement originates from at most one target. When tracking a high number of deformable sources, the previous assumptions are often not met, leading existing methods to fail. Here, we propose an algorithm which allows to perform the tracking in the cases when a single target generates several measurements or several targets generate a single measurement. The novel idea presented in this paper is the introduction of a set that we call virtual measurement set which supersedes and extends the set of measurements. This set is chosen to optimally fit the set of predicted measurements at each time step. This is done in two stages : i) a set of feasible joint association events is built from virtual measurements that are created by successively splitting and merging real measurements; ii) the joint probability is maximized over all feasible joint association events. The method has been tested on microscopy image sequences which typically contains densely moving objects and gives satisfactory preliminary results.