Object Tracking Via ImageNet Classification Scores

Object Tracking Via ImageNet Classification Scores
Title:
Object Tracking Via ImageNet Classification Scores
Other Titles:
2020 IEEE International Conference on Image Processing (ICIP)
Publication Date:
28 October 2020
Citation:
Abstract:
Object tracking is a challenging task in computer vision. The correlation filter based trackers are widely used for visual tracking due to their efficiencies. However, they cannot handle occlusion very well. In this paper, an effective method is proposed for occlusion detection based on high-level classification scores from the Convolutional Neural Network (CNN) trained on the ImageNet dataset. Also, we propose a novel tracking method by holistically considering multiple tracking models trained previously. In each frame, multiple correlation filters are first trained using hierarchical convolutional features, and then progressively selected according to the so-called tracking quality (status). Finally, a linear motion model is adopted to effectively re-detect the lost target. Experimental results have demonstrated that our method achieved good performance for handling occlusion.
License type:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Funding Info:
There is no specific source of funding.
Description:
“© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
ISSN:
2381-8549
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