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Gating validation and Variational Bayesian have been incorporated for multi target tracking problem.
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Our future work will focus on (1) the optimization of the features computation and (2) the multi-threading of the tracking in order to perform multi-target tracking with load balancing on the available core.
Occlusion between tracked objects is one of the most difficult problems in multi-target tracking.
FNF can effectively achieve multi-target tracking.
The authors used a probabilistic multi-target tracking for swarms of 6 to 25 mosquitoes.
Moreover, the dynamics models of multi-target tracking approaches do not handle properly noisy data.
Classical multi-target tracking consists of three sub-problems; detection, association, and estimation [39, 28].
MHT is another widely used technique for data association in multi-target tracking.
Bayesian filter is an efficient approach for multi-target tracking in the presence of clutter.
3D multi-target tracking based on the Kalman filter and Global Nearest Neighbour is then performed.
In this paper, we introduce a multi-target tracking algorithm suitable for team sports activities.
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