Tracklet Proposal Network for Multi-Object Tracking on Point Clouds Conference Paper uri icon

abstract

  • This paper proposes the first tracklet proposal network, named PC-TCNN, for Multi-Object Tracking (MOT) on point clouds. Our pipeline first generates tracklet proposals, then refines these tracklets and associates them to generate long trajectories. Specifically, object proposal generation and motion regression are first performed on a point cloud sequence to generate tracklet candidates. Then, spatial-temporal features of each tracklet are exploited and their consistency is used to refine the tracklet proposal. Finally, the refined tracklets across multiple frames are associated to perform MOT on the point cloud sequence. The PC-TCNN significantly improves the MOT performance by introducing the tracklet proposal design. On the KITTI tracking benchmark, it attains an MOTA of 91.75%, outperforming all submitted results on the online leaderboard.

name of conference

  • Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

published proceedings

  • Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence

author list (cited authors)

  • Wu, H., Li, Q., Wen, C., Li, X., Fan, X., & Wang, C.

citation count

  • 13

complete list of authors

  • Wu, Hai||Li, Qing||Wen, Chenglu||Li, Xin||Fan, Xiaoliang||Wang, Cheng

publication date

  • August 2021