Method

Stereo3DMOT: Stereo-based 3D Multi-Object Tracking with Re-identification [Stereo3DMOT]
https://github.com/Chain-Mao/Stereo3DMOT

Submitted on 28 Feb. 2024 14:42 by
Chen Mao (University of Chinese Academy of Sciences)

Running time:0.06 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We propose a 3D MOT system based on a cost-
effective stereo camera pair, which includes a 3D
multimodal re-identification (ReID) model capable
of multi-task learning. The ReID model obtains the
multimodal features of objects, including RGB and
point cloud information. We design data
association and trajectory management algorithms.
The data association computes an affinity matrix
for the object feature embeddings and motion
information, while the trajectory management
controls the lifecycle of the trajectories.
Parameters:
none
Latex Bibtex:
@inproceedings{mao2023stereo3dmot,
title={Stereo3DMOT: Stereo Vision Based 3D
Multi-object Tracking with Multimodal ReID},
author={Mao, Chen and Tan, Chong and Liu, Hong
and Hu, Jingqi and Zheng, Min},
booktitle={Chinese Conference on Pattern
Recognition and Computer Vision (PRCV)},
pages={495--507},
year={2023},
organization={Springer}
}

Detailed Results

From all 29 test sequences, our benchmark computes the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 77.32 % 73.43 % 81.86 % 77.34 % 85.38 % 84.66 % 89.61 % 86.72 %

Benchmark TP FP FN
CAR 30568 3824 585

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 87.10 % 85.06 % 87.18 % 28 73.82 %

Benchmark MT rate PT rate ML rate FRAG
CAR 75.69 % 14.92 % 9.38 % 662

Benchmark # Dets # Tracks
CAR 31153 681

This table as LaTeX


This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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