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

研究成果: Article査読

5 被引用数 (Scopus)


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 multiview 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.

ジャーナルIEICE Transactions on Information and Systems
出版ステータスPublished - 2018 5月

ASJC Scopus subject areas

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学
  • 人工知能


「Multi-peak estimation for real-time 3D ping-pong ball tracking with double-queue based GPU acceleration」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。