Robust Target Tracking Algorithm Based on Superpixel Visual Attention Mechanism

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Abstract
As existing target tracking algorithms are prone to drift under complex environments, the authors propose a tracking algorithm based on superpixel segmentation and visual attention mechanism. The algorithm works on a particle filter framework, by conducting a superpixel segmentation first and then building a model of the visual attention mechanism for saliently mapping. After extracting HOG features of salient regions, the authors compared the HOG feature with the target template at last, in order to locate the target region in the new frame. The proposed algorithm is evaluated on a comprehensive test platform, in which the simulation show that our tracker is more efficacious and more efficient than the previous traditional target tracking algorithms to cope with target drift issue.

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