IEEE Transactions on Circuits and Systems for Video Technology | 2019

Robust Visual Tracking Using Multi-Frame Multi-Feature Joint Modeling

 
 
 
 
 
 
 

Abstract


It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations. To cope with this problem, a promising solution is to integrate the temporal consistency across consecutive frames and multiple feature cues in a unified model. Motivated by this idea, we propose a novel correlation filter-based tracker in this paper, in which the temporal relatedness is reconciled under a multi-task learning framework and the multiple feature cues are modeled using a multi-view learning approach. We demonstrate that the resulting regression model can be efficiently learned by exploiting the structure of blockwise diagonal matrix. A fast blockwise diagonal matrix inversion algorithm is developed thereafter for efficient online tracking. Meanwhile, we incorporate an adaptive scale estimation mechanism to strengthen the stability of scale variation tracking. We implement our tracker using two types of features and test it on two benchmark datasets. The experimental results demonstrate the superiority of our proposed approach when compared with the other state-of-the-art trackers.

Volume 29
Pages 3673-3686
DOI 10.1109/TCSVT.2018.2882339
Language English
Journal IEEE Transactions on Circuits and Systems for Video Technology

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