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Method

Text-Guided 3D Multi-Object Tracking with RegionCLIP [TG3MOT_V1]


Submitted on 19 Oct. 2026 22:31 by
Yl LIU ()

Running time:2.2 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
TG3MOT_V1 extends YONTD-MOT with text-guided
semantic filtering. It uses RegionCLIP for 2D
detection and VoxelRCNN for 3D detection. This V1
configuration includes TSM without 3D F-EMA or GCF.
Parameters:
RegionCLIP: enabled
TSM: enabled
3D F-EMA: disabled
GCF: disabled
Car.confidence_threshold = 0.3
Car.min_frames = 1
max_ages = 48
confidence_his_max = 9
Latex Bibtex:
@article{wang2023you,
author = {Wang, Xiyang and He, Jiawei and Fu,
Chunyun and Meng, Ting and Huang, Mingguang},
title = {You Only Need Two Detectors to Achieve
Multi-Modal {3D} Multi-Object Tracking},
journal = {arXiv preprint arXiv:2304.08709},
year = {2023}
}

Detailed Results

From all 29 test sequences, our benchmark computes the commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 83.48 % 86.18 % 83.57 % 88.91 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 94.96 % 90.85 % 92.86 % 36714 3699 1950 33.25 % 48039 1065

Benchmark MT PT ML IDS FRAG
CAR 82.00 % 15.54 % 2.46 % 32 315

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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