Relational Dynamic Bayesian Networks to Improve Multi-target Tracking
Tracking relations between moving objects is a big challenge for Computer Vision research. Relations can be useful to better understand the behaviors of the targets, and the prediction of trajectories can become more accurate. Moreover, they can be useful in a variety of situations like monitoring terrorist activities, anomaly detection, sport coaching, etc.
In this paper we propose a model based on Relational Dynamic Bayesian Networks (RDBNs), that uses first-order logic to model particular correlations between objects behaviors, and show that the performance of the prediction increases significantly. In our experiments we consider the problem of multi-target tracking on a highway where the behavior of targets is often correlated to the behavior of the targets near to them. We compare the performance of a Particle Filter that does not take into account relations between objects and the performance of a Particle Filter that makes inference over the proposed RDBN.
We show that our method can follow the targets path more closely than the standard methods, being able to better predict their behaviors while decreasing the complexity of the tracker task.
KeywordsMarkov Chain Monte Carlo Particle Filter Transition Model Data Association Dynamic Bayesian Network
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