Authors:
Rui Figueiredo
1
;
João Avelino
1
;
Atabak Dehban
1
;
Alexandre Bernardino
1
;
Pedro Lima
1
and
Helder Araújo
2
Affiliations:
1
Instituto Superior Técnico, Portugal
;
2
Universidade de Coimbra, Portugal
Keyword(s):
Active Sensing, Constrained Resource Allocation, Multiple Object Tracking.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
;
Visual Attention and Image Saliency
Abstract:
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.
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