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Multiple Object Tracking 벤치마크

Multiple Object Tracking on KITTI Test (Online Methods)

102개 결과 · ⬇ CSV · JSON

HOTA

62.05 66.99 71.92 76.86 81.8 2019-07 2026-09 AB3DMOT — 69.99 (2019-07-09) AB3DMOT — 69.99 (2019-07-09) AB3DMOT — 69.99 (2019-07-09) mmMOT — 62.05 (2019-09-09) mmMOT — 62.05 (2019-09-09) mmMOT — 62.05 (2019-09-09) JRMOT — 69.61 (2020-02-19) JRMOT — 69.61 (2020-02-19) JRMOT — 69.61 (2020-02-19) CenterTrack — 73.02 (2020-04-02) CenterTrack — 73.02 (2020-04-02) CenterTrack — 73.02 (2020-04-02) DEFT — 74.23 (2021-02-03) DEFT — 74.23 (2021-02-03) DEFT — 74.23 (2021-02-03) QD-3DT — 72.77 (2021-03-12) QD-3DT — 72.77 (2021-03-12) QD-3DT — 72.77 (2021-03-12) EagerMOT — 74.39 (2021-04-29) EagerMOT — 74.39 (2021-04-29) EagerMOT — 74.39 (2021-04-29) OC-SORT — 76.5 (2022-03-27) OC-SORT — 76.5 (2022-03-27) OC-SORT — 76.5 (2022-03-27) VirConvTrack — 79.9 (2023-03-04) VirConvTrack — 79.9 (2023-03-04) VirConvTrack — 79.9 (2023-03-04) LEGO — 80.75 (2023-08-19) LEGO — 80.75 (2023-08-19) LEGO — 80.75 (2023-08-19) UCMCTrack — 77.1 (2023-12-14) UCMCTrack — 77.1 (2023-12-14) UCMCTrack — 77.1 (2023-12-14) C-TWiX — 77.58 (2024-03-12) C-TWiX — 77.58 (2024-03-12) C-TWiX — 77.58 (2024-03-12) RobMOT — 81.76 (2024-05-19) RobMOT — 81.76 (2024-05-19) RobMOT — 81.76 (2024-05-19) IMM-JHSE — 79.21 (2024-09-04) IMM-JHSE — 79.21 (2024-09-04) IMM-JHSE — 79.21 (2024-09-04) MCTrack — 81.07 (2024-09-23) MCTrack — 81.07 (2024-09-23) MCTrack — 81.07 (2024-09-23) RobMOT (Dynamic) — 81.8 (2025-05-12) RobMOT (Dynamic) — 81.8 (2025-05-12) RobMOT (Dynamic) — 81.8 (2025-05-12) AB3DMOT — 69.99 (2019-07-09) CenterTrack — 73.02 (2020-04-02) DEFT — 74.23 (2021-02-03) EagerMOT — 74.39 (2021-04-29) OC-SORT — 76.5 (2022-03-27) VirConvTrack — 79.9 (2023-03-04) LEGO — 80.75 (2023-08-19) RobMOT — 81.76 (2024-05-19) RobMOT (Dynamic) — 81.8 (2025-05-12)
RankModel HOTAMOTAIDSW PaperCodeYear
1 RobMOT (Dynamic) 81.8091.1113 Towards Accurate State Estimation: Kalman Filter Incorporating Motion Dynamics for 3D Multi-Object Tracking 2025
2 RobMOT 81.7691.027 RobMOT: Robust 3D Multi-Object Tracking by Observational Noise and State Estimation Drift Mitigation on LiDAR PointCloud MohamedNagyMostafa/RobMOT 2024
3 MCTrack 81.0789.8264 MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving megvii-research/mctrack 2024
4 KFDL 81.0690.2922
5 PC-TCNN 80.9078.4637
6 LEGO 80.7590.61214 LEGO: Learning and Graph-Optimized Modular Tracker for Online Multi-Object Tracking with Point Clouds 2023
7 VirConvTrack 79.989.1201 Virtual Sparse Convolution for Multimodal 3D Object Detection hailanyi/virconv 2023
8 IMM-JHSE 79.2189.8177 One Homography is All You Need: IMM-based Joint Homography and Multiple Object State Estimation Paulkie99/imm-jhse 2024
9 C-TWiX 77.5889.68381 Learning Data Association for Multi-Object Tracking using Only Coordinates Guepardow/TWiX 2024
10 CasTrack 77.386,29184
11 UCMCTrack 77.190.4– UCMCTrack: Multi-Object Tracking with Uniform Camera Motion Compensation corfyi/ucmctrack 2023
12 OC-SORT 76.590.3250 Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking PaddlePaddle/PaddleDetection · mikel-brostrom/boxmot · open-mmlab/mmtracking · +4 2022
13 EagerMOT 74.3987.82239 EagerMOT: 3D Multi-Object Tracking via Sensor Fusion aleksandrkim61/EagerMOT · JrkJamie/eagermot_ms · gai-shaoyan/mind3d 2021
14 DEFT 74.23 88.95– DEFT: Detection Embeddings for Tracking MedChaabane/DEFT 2021
15 CenterTrack 73.0289.44– Tracking Objects as Points PaddlePaddle/PaddleDetection · xingyizhou/CenterTrack · JialianW/TraDeS · +4 2020
16 QD-3DT 72.77 86.41– Monocular Quasi-Dense 3D Object Tracking SysCV/qd-3dt 2021
17 AB3DMOT 69.99 83.84– 3D Multi-Object Tracking: A Baseline and New Evaluation Metrics xinshuoweng/AB3DMOT 2019
18 JRMOT 69.61 85.70– JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset StanfordVL/JRMOT_ROS 2020
19 mmMOT 62.0584.77– Robust Multi-Modality Multi-Object Tracking ZwwWayne/mmMOT 2019
20 SRK ODESA –90.03– Learning Local Feature Descriptors for Multiple Object Tracking 2020
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