https://openaccess.thecvf.com/content/ICCV2021/html/Luo_Exploring_Simple_3D_Multi-Object_Tracking_for_Autonomous_Driving_ICCV_2021_paper.html ICCV 2021 Open Access Repository Exploring Simple 3D Multi-Object Tracking for Autonomous Driving Chenxu Luo, Xiaodong Yang, Alan Yuille; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10488-10497 Abstract 3D mult..
https://arxiv.org/abs/2110.06864 ByteTrack: Multi-Object Tracking by Associating Every Detection Box Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects in videos. Most methods obtain identities by associating detection boxes whose scores are higher than a threshold. The objects with low detection scores, e.g. occluded arxiv.org 이 논문은 data association 기법을 제안하는..
https://arxiv.org/abs/2012.15460 TransTrack: Multiple Object Tracking with Transformer In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an attention-based query-key mechanism. It applies object features from the previ arxiv.org 이번에 리뷰할 논문은 2D MOT 논문으로 transformer를 이용한 Joi..
https://arxiv.org/abs/2108.10312 Exploring Simple 3D Multi-Object Tracking for Autonomous Driving 3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving vehicles. Existing methods are predominantly based on the tracking-by-detection pipeline and inevitably require a heuristic matching step for the detection association. In arxiv.org 이번에 review할 논문은 3D MOT 논문 중 Joint ..
