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Object tracking and detection after occlusion via numerical hybrid local and global mode-seeking

12 years 10 months ago
Object tracking and detection after occlusion via numerical hybrid local and global mode-seeking
Given an object model and a black-box measure of similarity between the model and candidate targets, we consider visual object tracking as a numerical optimization problem. During normal tracking conditions when the object is visible from frame to frame, local optimization is used to track the local mode of the similarity measure in a parameter space of translation, rotation and scale. However, when the object becomes partially or totally occluded, such local tracking is prone to failure, especially when common prediction techniques like the Kalman filter do not provide a good estimate of object parameters in future frames. To recover from these inevitable tracking failures, we consider object detection as a global optimization problem and solve it via Adaptive Simulated Annealing (ASA), a method that avoids becoming trapped at local modes and is much faster than exhaustive search. As a Monte Carlo approach, ASA stochastically samples the parameter space, in contrast to local determin...
Zhaozheng Yin, Robert T. Collins
Added 12 Oct 2009
Updated 28 Oct 2009
Type Conference
Year 2008
Where CVPR
Authors Zhaozheng Yin, Robert T. Collins
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