Correlation Tracking via Self-Adaptive Fusion of Multiple Features

• Published in: Information (Basel) Vol. 9; no. 10; p. 241
• Main Authors: Chen, Zhi, Liu, Peizhong, Du, Yongzhao, Luo, Yanmin, Zhang, Wancheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 2018
• Subjects: Adaptive algorithms; Algorithms; Clutter; Correlation; correlation filter; Distance learning; Histograms; Methods; model update; Model updating; multiple features; Occlusion; Power; Principal components analysis; R&D; Research & development; self-adaptive fusion; Tracking; visual tracking
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info9100241

Correlation filter (CF) based tracking algorithms have shown excellent performance in comparison to most state-of-the-art algorithms on the object tracking benchmark (OTB). Nonetheless, most CF based tracking algorithms only consider limited single channel feature, and the tracking model always updated from frame-by-frame. It will generate some erroneous information when the target objects undergo sophisticated scenario changes, such as background clutter, occlusion, out-of-view, and so forth. Long-term accumulation of erroneous model updating will cause tracking drift. In order to address problems that are mentioned above, in this paper, we propose a robust multi-scale correlation filter tracking algorithm via self-adaptive fusion of multiple features. First, we fuse powerful multiple features including histogram of oriented gradients (HOG), color name (CN), and histogram of local intensities (HI) in the response layer. The weights assigned according to the proportion of response scores that are generated by each feature, which achieve self-adaptive fusion of multiple features for preferable feature representation. In the meantime the efficient model update strategy is proposed, which is performed by exploiting a pre-defined response threshold as discriminative condition for updating tracking model. In addition, we introduce an accurate multi-scale estimation method integrate with the model update strategy, which further improves the scale variation adaptability. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed tracker performs superiorly against the state-of-the-art CF based methods.