Multi-Peak Estimation for Real-Time 3D Ping-Pong Ball Tracking with Double-Queue Based GPU Acceleration

Ziwei DENG  Yilin HOU  Xina CHENG  Takeshi IKENAGA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.5   pp.1251-1259
Publication Date: 2018/05/01
Publicized: 2018/02/16
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017MVP0010
Type of Manuscript: Special Section PAPER (Special Section on Machine Vision and its Applications)
Category: Machine Vision and its Applications
Keyword: 
3D ball tracking,  GPU acceleration,  heterogeneous computing,  OpenCL,  particle filter,  sports analysis,  

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Summary: 
3D ball tracking is of great significance in ping-pong game analysis, which can be utilized to applications such as TV contents and tactic analysis, with some of them requiring real-time implementation. This paper proposes a CPU-GPU platform based Particle Filter for multi-view ball tracking including 4 proposals. The multi-peak estimation and the ball-like observation model are proposed in the algorithm design. The multi-peak estimation aims at obtaining a precise ball position in case the particles' likelihood distribution has multiple peaks under complex circumstances. The ball-like observation model with 4 different likelihood evaluation, utilizes the ball's unique features to evaluate the particle's similarity with the target. In the GPU implementation, the double-queue structure and the vectorized data combination are proposed. The double-queue structure aims at achieving task parallelism between some data-independent tasks. The vectorized data combination reduces the time cost in memory access by combining 3 different image data to 1 vector data. Experiments are based on ping-pong videos recorded in an official match taken by 4 cameras located in 4 corners of the court. The tracking success rate reaches 99.59% on CPU. With the GPU acceleration, the time consumption is 8.8 ms/frame, which is sped up by a factor of 98 compared with its CPU version.