A visual tracking method via object detection based on deep learning

被引：0

作者：

Tang C. ^{[1
,2
]}

Ling Y. ^{[1
,2
]}

Yang H. ^{[1
,2
]}

Yang X. ^{[1
,2
]}

Zheng C. ^{[1
,2
]}

机构：

[1] National University of Defense Technology, Hefei

[2] State Key Laboratory of Pulsed Power Laser Technology, Hefei

来源：

Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering | 2018年 / 47卷 / 05期

关键词：

Deep learning; Non-online updating; SSD; Visual tracking;

D O I：

10.3788/IRLA201847.0526001

中图分类号：

学科分类号：

摘要：

A visual tracking method via object detection based on deep learning was proposed. In consideration of the advantages of deep learning in feature representation, deep model SSD (Single Shot Multibox Detector) was used as the candidate object extractor in the tracking model. Simultaneously, the color histogram feature and HOG (Histogram of Oriented Gradient) feature were combined to select the tracking object. In the process of tracking, multi-scale object searching map, which was applied to implement the object detection in different scales, was built to improve the detection performance of deep learning model. In the experiment of eight respective tracking video sequences in the baseline dataset, compared with six typical tracking methods, the proposed method has better performance in tracking effect, and has better robustness in the tracking challenging factors, such as deformation, scale variation, rotation variation, illumination variation, and background clutters. © 2018, Editorial Board of Journal of Infrared and Laser Engineering. All right reserved.

引用

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