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http://dx.doi.org/10.3837/tiis.2020.01.012

Efficient Parallel TLD on CPU-GPU Platform for Real-Time Tracking  

Chen, Zhaoyun (College of Computer, National University of Defense Technology)
Huang, Dafei (Southwest Electronics and Telecommunication Technology Research Institute)
Luo, Lei (College of Computer, National University of Defense Technology)
Wen, Mei (College of Computer, National University of Defense Technology)
Zhang, Chunyuan (College of Computer, National University of Defense Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.1, 2020 , pp. 201-220 More about this Journal
Abstract
Trackers, especially long-term (LT) trackers, now have a more complex structure and more intensive computation for nowadays' endless pursuit of high accuracy and robustness. However, computing efficiency of LT trackers cannot meet the real-time requirement in various real application scenarios. Considering heterogeneous CPU-GPU platforms have been more popular than ever, it is a challenge to exploit the computing capacity of heterogeneous platform to improve the efficiency of LT trackers for real-time requirement. This paper focuses on TLD, which is the first LT tracking framework, and proposes an efficient parallel implementation based on OpenCL. In this paper, we firstly make an analysis of the TLD tracker and then optimize the computing intensive kernels, including Fern Feature Extraction, Fern Classification, NCC Calculation, Overlaps Calculation, Positive and Negative Samples Extraction. Experimental results demonstrate that our efficient parallel TLD tracker outperforms the original TLD, achieving the 3.92 speedup on CPU and GPU. Moreover, the parallel TLD tracker can run 52.9 frames per second and meet the real-time requirement.
Keywords
TLD tracker; Real-Time; Heterogeneous Platform; OpenCL; Parallel Optimizations;
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