Visual Tracking Using Attention-Modulated 
Disintegration and Integration

OOTB2013 video


In this paper, we present a novel attention-modulated visual tracking algorithm that decomposes an object into into multiple cognitive units, and trains multiple elementary trackers in order 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


11/29, 2017  Github open
05/23, 2017  Benchmark results for TPAMI2015 dataset and VOT2014 were uploaded.
06/24, 2016  SCT4 program was uploaded.
06/23, 2016  Paper & bibtex were uploaded.
06/20, 2016  SCT4 was submitted to VOT challenge 2016.
04/04, 2016  Project page was built.
03/10, 2016  The conference paper was accepted in CVPR2016.


Visual Tracking Using Attention-Modulated Disintegration and Integration
Jongwon Choi, Hyung Jin Chang, Jiyeoup Jeong, Yiannis Demiris, and Jin Young Choi
IEEE Conference on Computer Vision and Pattern Recognition 2016 (CVPR2016), Accepted. [Poster]

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