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Proceedings Paper

An improved TLD object tracking algorithm
Author(s): Ting Li; Wen-jie Zhao; Shuai Yang; Cheng Li
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Paper Abstract

Although TLD (Tracking-Learning-Detection) algorithm can enable the long-term tracking, there are still many problems in it. In this paper, an improvement is made on the detection module of TLD to satisfy the need of time and accuracy. First, we use the Kalman Filter to narrow the detection range of the detector effectively. Then, we replace the traditional detector with Cascaded Random Forest detector, combining the global and local search strategy, which can reduce the computation burden of the algorithm, and achieve the real-time object tracking. The experimental results on various benchmark video sequences show that the proposed approaches compared with the traditional tracking algorithms not only presents robustness and tracking accuracy in stable background or complex conditions, but also obtains the best computing speed with the use of the Cascaded Random Forest.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100330H (29 August 2016); doi: 10.1117/12.2244919
Show Author Affiliations
Ting Li, Aviation Univ. of Air Force (China)
Wen-jie Zhao, Aviation Univ. of Air Force (China)
Shuai Yang, Aviation Univ. of Air Force (China)
Cheng Li, Aviation Univ. of Air Force (China)

Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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