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Sensors 2016, 16(9), 1449; doi:10.3390/s16091449

Real-Time Tracking Framework with Adaptive Features and Constrained Labels

1
School of Optoelectronics, Image Engineering & Video Technology Lab, Beijing Institute of Technology, Beijing 100081, China
2
Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 5 July 2016 / Revised: 22 August 2016 / Accepted: 22 August 2016 / Published: 8 September 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [4334 KB, uploaded 8 September 2016]   |  

Abstract

This paper proposes a novel tracking framework with adaptive features and constrained labels (AFCL) to handle illumination variation, occlusion and appearance changes caused by the variation of positions. The novel ensemble classifier, including the Forward–Backward error and the location constraint is applied, to get the precise coordinates of the promising bounding boxes. The Forward–Backward error can enhance the adaptation and accuracy of the binary features, whereas the location constraint can overcome the label noise to a certain degree. We use the combiner which can evaluate the online templates and the outputs of the classifier to accommodate the complex situation. Evaluation of the widely used tracking benchmark shows that the proposed framework can significantly improve the tracking accuracy, and thus reduce the processing time. The proposed framework has been tested and implemented on the embedded system using TMS320C6416 and Cyclone Ⅲ kernel processors. The outputs show that achievable and satisfying results can be obtained. View Full-Text
Keywords: real-time tracking framework; Forward–Backward error; ensemble classifier; location constraint; embedded system real-time tracking framework; Forward–Backward error; ensemble classifier; location constraint; embedded system
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, D.; Xu, T.; Chen, S.; Zhang, J.; Jiang, S. Real-Time Tracking Framework with Adaptive Features and Constrained Labels. Sensors 2016, 16, 1449.

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