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Proceedings Paper

Adaptive and accelerated tracking-learning-detection
Author(s): Pengyu Guo; Xin Li; Shaowen Ding; Zunhua Tian; Xiaohu Zhang
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Paper Abstract

An improved online long-term visual tracking algorithm, named adaptive and accelerated TLD (AA-TLD) based on Tracking-Learning-Detection (TLD) which is a novel tracking framework has been introduced in this paper. The improvement focuses on two aspects, one is adaption, which makes the algorithm not dependent on the pre-defined scanning grids by online generating scale space, and the other is efficiency, which uses not only algorithm-level acceleration like scale prediction that employs auto-regression and moving average (ARMA) model to learn the object motion to lessen the detector’s searching range and the fixed number of positive and negative samples that ensures a constant retrieving time, but also CPU and GPU parallel technology to achieve hardware acceleration. In addition, in order to obtain a better effect, some TLD’s details are redesigned, which uses a weight including both normalized correlation coefficient and scale size to integrate results, and adjusts distance metric thresholds online. A contrastive experiment on success rate, center location error and execution time, is carried out to show a performance and efficiency upgrade over state-of-the-art TLD with partial TLD datasets and Shenzhou IX return capsule image sequences. The algorithm can be used in the field of video surveillance to meet the need of real-time video tracking.

Paper Details

Date Published: 21 August 2013
PDF: 10 pages
Proc. SPIE 8908, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Sensors and Applications, 89082H (21 August 2013); doi: 10.1117/12.2034977
Show Author Affiliations
Pengyu Guo, National Univ. of Defense Technology (China)
Xin Li, National Univ. of Defense Technology (China)
Shaowen Ding, National Univ. of Defense Technology (China)
Zunhua Tian, National Univ. of Defense Technology (China)
Xiaohu Zhang, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 8908:
International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Sensors and Applications
Jun Ohta; Nanjian Wu; Binqiao Li, Editor(s)

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