Towards Robust Multi-cue Integration for Visual Tracking
ICVS '01 Proceedings of the Second International Workshop on Computer Vision Systems
Towards Robust Perception and Model Integration
Revised Papers from the International Workshop on Sensor Based Intelligent Robots
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We study the dynamics of visual cue integration in a tracking / identification task, where subjects track a target object among distractors and identify the target after an occlusion. Objects are defined by three different attributes (color, shape, size) which change randomly within a singe trial. When the attributes differ in their reliability (two change frequently, one is stable) our results show that subjects rapidly re-weight the different cues, putting more emphasis on the stable cue. The re-weighting takes place in less than one second. Our results suggest that cue integration can exhibit adaptive phenomena on a very fast time scale. We propose a probabilistic model with temporal dynamics that accounts for the observed effect.