Paper
21 August 2013 Adaptive and accelerated tracking-learning-detection
Pengyu Guo, Xin Li, Shaowen Ding, Zunhua Tian, Xiaohu Zhang
Author Affiliations +
Proceedings Volume 8908, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Sensors and Applications; 89082H (2013) https://doi.org/10.1117/12.2034977
Event: ISPDI 2013 - Fifth International Symposium on Photoelectronic Detection and Imaging, 2013, Beijing, China
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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Pengyu Guo, Xin Li, Shaowen Ding, Zunhua Tian, and Xiaohu Zhang "Adaptive and accelerated tracking-learning-detection", Proc. SPIE 8908, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Sensors and Applications, 89082H (21 August 2013); https://doi.org/10.1117/12.2034977
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Sensors

Detection and tracking algorithms

Optical tracking

Video surveillance

Electroluminescence

Motion models

Particle filters

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