PointTrackNet：点群からの3次元物体検出・追跡を統合するエンドツーエンドネットワーク
PointTrackNet: An End-to-End Network For 3-D Object Detection and Tracking From Point Clouds
隣接する2フレームの点群を入力とし、前景マスク・3次元バウンディングボックス・点単位の追跡対応変位を同時に出力するエンドツーエンドの3次元物体検出・追跡ネットワークを提案した。急ブレーキや急旋回など不規則で急激な運動にも頑健で、KITTI追跡データセットで競争力のある結果を示した。
著者: Sukai Wang, Yuxiang Sun, Chengju Liu, Ming Liu
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Recent machine learning-based multi-object tracking (MOT) frameworks are becoming popular for 3-D point clouds. Most traditional tracking approaches use filters (e.g., Kalman filter or particle filter) to predict object locations in a time sequence, however, they are vulnerable to extreme motion conditions, such as sudden braking and turning. In this letter, we propose PointTrackNet, an end-to-end 3-D object detection and tracking network, to generate foreground masks, 3-D bounding boxes, and point-wise tracking association displacements for each detected object. The network merely takes as input two adjacent point-cloud frames. Experimental results on the KITTI tracking dataset show competitive results over the state-of-the-arts, especially in the irregularly and rapidly changing scenarios.
