Long-term correlation tracking using multi-layer hybrid features in sparse and dense environments

Baisa, Nathanael L. and Bhowmik, Deepayan and Wallace, Andrew (2018) Long-term correlation tracking using multi-layer hybrid features in sparse and dense environments. Journal of Visual Communication and Image Representation, 55 . 464 - 476. ISSN 1047-3203

Full content URL: https://doi.org/10.1016/j.jvcir.2018.06.027

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Long-term correlation tracking using multi-layer hybrid features in sparse and dense environments
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Abstract

Tracking a target of interest in both sparse and crowded environments is a challenging problem, not yet successfully addressed in the literature. In this paper, we propose a new long-term visual tracking algorithm, learning discriminative correlation filters and using an online classifier, to track a target of interest in both sparse and crowded video sequences. First, we learn a translation correlation filter using a multi-layer hybrid of convolutional neural networks (CNN) and traditional hand-crafted features. Second, we include a re-detection module for overcoming tracking failures due to long-term occlusions using online SVM and Gaussian mixture probability hypothesis density (GM-PHD) filter. Finally, we learn a scale correlation filter for estimating the scale of a target by constructing a target pyramid around the estimated or re-detected position using the HOG features. We carry out extensive experiments on both sparse and dense data sets which show that our method significantly outperforms state-of-the-art methods.

Additional Information:The final published version of this article can be accessed online at https://www.sciencedirect.com/science/article/pii/S1047320318301536
Keywords:Visual tracking, Correlation filter, CNN features, Hybrid features, Online learning, GM-PHD filter
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:33092
Deposited On:13 Sep 2018 10:59

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