Multi-camera multi-player tracking with deep player identification in sports video

作者：

Highlights：

• We propose a robust tracking framework for basketball players in the multi-camera sports videos.

• We introduce deep-learning-based player identification into the player tracker to leverage the players ID.

• The framework consists of three modules: DeepPlayer, IPOM and KSP-ID. A Cascade Mask RCNN and a PoseID model are designed for the DeepPlayer.

• Our framework yields superior performance in two benchmarks.

摘要

•We propose a robust tracking framework for basketball players in the multi-camera sports videos.•We introduce deep-learning-based player identification into the player tracker to leverage the players ID.•The framework consists of three modules: DeepPlayer, IPOM and KSP-ID. A Cascade Mask RCNN and a PoseID model are designed for the DeepPlayer.•Our framework yields superior performance in two benchmarks.

论文关键词：Identity switch,Multi-target multi-camera tracking,Object detection,Player identification,CNN

论文评审过程：Received 6 June 2019, Revised 15 January 2020, Accepted 2 February 2020, Available online 3 February 2020, Version of Record 8 February 2020.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107260