Abstract
3D ball tracking is of great significance to sports analysis, which can be utilized to applications such as TV contents and tactic analysis. Some applications require real-time implementation, but a highly accurate tracking algorithm is usually time-consuming. This paper proposes a CPU-GPU platform based particle filter for multi-view ball tracking, including 2 proposals: vectorized mask data combination and binary search oriented reweight. The vectorized masks data combination unites HSV mask and inter-frame subtraction mask into one to reduce memory access time. The binary search oriented reweight helps getting and saving reweighted data with low complexity which could directly be used for binary search. The proposed methods are evaluated by both tracking accuracy and execution time. Experiment is based on GPU, the AMD R9 Fury, and compared to the serial implementation on CPU. The tracking accuracy keeps the same, while the execution time is reduced by a factor of 13.
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Acknowledgments
This work was supported by KAKENHI (16K 13006) and Waseda University Grant for Special Research Projects (2017K-263).
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Deng, Z., Hou, Y., Cheng, X., Ikenaga, T. (2018). Vectorized Data Combination and Binary Search Oriented Reweight for CPU-GPU Based Real-Time 3D Ball Tracking. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_50
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DOI: https://doi.org/10.1007/978-3-319-77383-4_50
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