Abstract
Multi-target tracking in video has been a research focus, with the combination of many fields, such as computer vision, artificial intelligence, pattern matching. In this paper, we present an efficient multi-target recognition and tracking algorithm based on TLD (Tracking-Learning-Detection), named PE-TLD. A new foreground extraction filter using ViBe is introduced to improve the speed and accuracy of detection. A new target recognition component is added, and core detector is improved. Based on that, we further implemented a parallel version, taking advantage of the state-of-the-art parallel computing techniques such as OpenMP and OpenCL, which runs efficiently on a system with both multi-core CPU and GPU. Experiments showed that PE-TLD is up to 5 times faster than the serial version. PE-TLD is an automatic multi-target recognition and tracking system, which is efficient enough to be deployed for real-time usage.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China under Grant No. 61212005, 61303059, and the Natural Science Foundation of Tianjin, China under Grant No. 14JCTPJC00501.
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Zhou, C., Dong, Q., Ma, W., Long, G., Li, T. (2015). PE-TLD: Parallel Extended Tracking-Learning-Detection for Multi-target Tracking. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9529. Springer, Cham. https://doi.org/10.1007/978-3-319-27122-4_46
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DOI: https://doi.org/10.1007/978-3-319-27122-4_46
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