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[CVPR'16] Visual Tracking Using Attention-Modulated Disintegration and Integration
In this paper, we present a novel attention-modulated visual tracking algorithm that decomposes an object into multiple cognitive units and trains multiple elementary trackers to modulate the distribution of attention according to various feature and kernel types. In the integration stage, it recombines the units to memorize and recognize the target object effectively. With respect to the elementary trackers, we present a novel attentional feature-based correlation filter (AtCF) that focuses on distinctive attentional features. The effectiveness of the proposed algorithm is validated through experimental comparison with state-of-the-art methods on widely-used tracking benchmark datasets.
Fig 1. Framework for the proposed tracker
Fig 2. Tracking performance obtained by OOTB2013 dataset