Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning

判别式 计算机科学 人工智能 模式识别(心理学) BitTorrent跟踪器 水准点(测量) 分类器(UML) K-SVD公司 眼动 词典学习 秩(图论) 机器学习 稀疏逼近 数学 大地测量学 组合数学 地理
作者
Tao Zhou,Fanghui Liu,Harish Bhaskar,Jie Yang
出处
期刊:IEEE transactions on cybernetics [Institute of Electrical and Electronics Engineers]
卷期号:48 (9): 2643-2655 被引量:43
标识
DOI:10.1109/tcyb.2017.2747998
摘要

In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.

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