物体検出とシーンフロー推定を同時最適化する3Dマルチオブジェクト追跡
DetFlowTrack: 3D Multi-object Tracking based on Simultaneous Optimization of Object Detection and Scene Flow Estimation
物体検出とシーンフロー推定を同時に最適化する3Dマルチオブジェクト追跡フレームワークを提案し、回転を伴う運動でも頑健な追跡を実現した。
著者: Yueling Shen, Guangming Wang, Hesheng Wang
分類: cs.CV, cs.RO
原文アブストラクト
3D Multi-Object Tracking (MOT) is an important part of the unmanned vehicle perception module. Most methods optimize object detection and data association independently. These methods make the network structure complicated and limit the improvement of MOT accuracy. we proposed a 3D MOT framework based on simultaneous optimization of object detection and scene flow estimation. In the framework, a detection-guidance scene flow module is proposed to relieve the problem of incorrect inter-frame assocation. For more accurate scene flow label especially in the case of motion with rotation, a box-transformation-based scene flow ground truth calculation method is proposed. Experimental results on the KITTI MOT dataset show competitive results over the state-of-the-arts and the robustness under extreme motion with rotation.
