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A visual tracking method based on deep learning without online model updating
Author(s): Cong Tang; Yicheng Wang; Yunsong Feng; Chao Zheng; Wei Jin
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Paper Abstract

The paper proposes a visual tracking method based on deep learning without online model updating. In consideration of the advantages of deep learning in feature representation, deep model SSD (Single Shot Multibox Detector) is used as the object extractor in the tracking model. Simultaneously, the color histogram feature and HOG (Histogram of Oriented Gradient) feature are combined to select the tracking object. In the process of tracking, multi-scale object searching map is built to improve the detection performance of deep detection model and the tracking efficiency. In the experiment of eight respective tracking video sequences in the baseline dataset, compared with six state-of-the-art methods, the method in the paper has better robustness in the tracking challenging factors, such as deformation, scale variation, rotation variation, illumination variation, and background clutters, moreover, its general performance is better than other six tracking methods.

Paper Details

Date Published: 20 February 2018
PDF: 9 pages
Proc. SPIE 10697, Fourth Seminar on Novel Optoelectronic Detection Technology and Application, 1069726 (20 February 2018); doi: 10.1117/12.2315389
Show Author Affiliations
Cong Tang, National Univ. of Defense Technology (China)
Yicheng Wang, National Univ. of Defense Technology (China)
Yunsong Feng, National Univ. of Defense Technology (China)
Chao Zheng, National Univ. of Defense Technology (China)
Wei Jin, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 10697:
Fourth Seminar on Novel Optoelectronic Detection Technology and Application
Weiqi Jin; Ye Li, Editor(s)

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