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In this work we address the multiple person tracking problem with resource constraints, which plays a fundamental role in the deployment of efficient mobile robots for real-time applications involved in Human Robot Interaction. We pose the multiple target tracking as a selective attention problem in which the perceptual agent tries to optimize the overall expected tracking accuracy. More specifically, we propose a resource constrained Partially Observable Markov Decision Process (POMDP) formulation that allows for real-time on-line planning. Using a transition model, we predict the true state from the current belief for a finite-horizon, and take actions to maximize future expected belief-dependent rewards. These rewards are based on the anticipated observation qualities, which are provided by an observation model that accounts for detection errors due to the discrete nature of a state-of-the-art pedestrian detector. Finally, a Monte Carlo Tree Search method is employed to solve the planning problem in real-time. The experiments show that directing the attentional focci to relevant image sub-regions allows for large detection speed-ups and improvements on tracking precision.

Efficient Resource Allocation for Sparse Multiple Object Tracking