Long-Term correlation tracking using multi-layer hybrid features in dense environments

BAISA, N.L., BHOWMIK, Deepayan and WALLACE, A. (2017). Long-Term correlation tracking using multi-layer hybrid features in dense environments. In: VISIGRAPP 2017: Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SCITEPRESS - Science and Technology Publications, 192-203.

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Link to published version:: https://doi.org/10.5220/0006117301920203


© 2017 by SCITEPRESS - Science and Technology Publications, Lda. Tracking a target of interest in crowded environments is a challenging problem, not yet successfully addressed in the literature. In this paper, we propose a new long-Term algorithm, learning a discriminative correlation filter and using an online classifier, to track a target of interest in dense video sequences. First, we learn a translational correlation filter using a multi-layer hybrid of convolutional neural networks (CNN) and traditional hand-crafted features. We combine the advantages of both the lower convolutional layer which retains better spatial detail for precise localization, and the higher convolutional layer which encodes semantic information for handling appearance variations. This is integrated with traditional features formed from a histogram of oriented gradients (HOG) and color-naming. Second, we include a re-detection module for overcoming tracking failures due to long-Term occlusions by training an incremental (online) SVM on the most confident frames using hand-engineered features. This re-detection module is activated only when the correlation response of the object is below some pre-defined threshold to generate high score detection proposals. Finally, we incorporate a Gaussian mixture probability hypothesis density (GM-PHD) filter to temporally filter high score detection proposals generated from the learned online SVM to find the detection proposal with the maximum weight as the target position estimate by removing the other detection proposals as clutter. Extensive experiments on dense data sets show that our method significantly outperforms state-of-The-Art methods.

Item Type: Book Section
Uncontrolled Keywords: Visual Tracking; Correlation Filter; CNN Features; Hybrid Features; Online Learning; GM-PHD Filter; cs.CV; cs.CV
Identification Number: https://doi.org/10.5220/0006117301920203
Page Range: 192-203
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 11 Mar 2019 13:00
Last Modified: 18 Mar 2021 06:26
URI: https://shura.shu.ac.uk/id/eprint/23190

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