Share Email Print

Proceedings Paper

A robust anti-occlusion object tracking method
Author(s): Qiang Fan; Bo Lei; Hai Tan
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Visual object tracking is one of the most attractive issue in computer vision. Recently, deep neural network has been widely developed in object tracking and showing great accuracy. In general, the accuracy of tracking task decreases dramatically when the background becomes complex or occluded. Thus, a robust tracking method based on convolutional neural network and anti-occlusion mechanic is presented. Benefit from the adaptive tracking confidence parameter T, the tracking effect is evaluated during tracking. Once the target is occluded, the location of the target object is corrected immediately. Experimental results demonstrate that the proposed framework achieves state-of-the-art performance on the popular OTB50 and OTB100 benchmarks.

Paper Details

Date Published: 14 August 2019
PDF: 7 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111793K (14 August 2019); doi: 10.1117/12.2539641
Show Author Affiliations
Qiang Fan, Huazhong Institute of Electro-Optics (China)
Bo Lei, Huazhong Institute of Electro-Optics (China)
Hai Tan, Huazhong Institute of Electro-Optics (China)

Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?