Method

End-to-End Multiple-Object 001 001 Tracking Method with YOLO and Decoder [MO-YOLO]
https://github.com/liaopan-lp/MO-YOLO

Submitted on 21 Mar. 2024 06:27 by
Pan Liao (西北工业大学)

Running time:0.024 s
Environment:2080ti (Python)

Method Description:
Drawing insights from successful models such as
GPT, our proposed MO-YOLO stands out as an
efficient and computationally frugal end-to-end
MOT solution. MO-YOLO integrates principles from
YOLO and RT-DETR, adopting a decoder-centric
architecture alongside other complementary
structures.
Parameters:
45M
Latex Bibtex:
@article{pan2023mo,
title={MO-YOLO: End-to-End Multiple-Object
Tracking Method with YOLO and MOTR},
author={Pan, Liao and Feng, Yang and Di, Wu and
Bo, Liu and Xingle, Zhang},
journal={arXiv preprint arXiv:2310.17170},
year={2023}
}

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 72.08 % 71.02 % 73.84 % 75.59 % 83.52 % 77.75 % 86.41 % 86.04 %
PEDESTRIAN 51.46 % 45.59 % 58.39 % 50.18 % 68.98 % 63.35 % 72.93 % 77.86 %

Benchmark TP FP FN
CAR 30007 4385 1121
PEDESTRIAN 15082 8068 1759

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 83.19 % 84.39 % 83.99 % 275 69.57 %
PEDESTRIAN 56.84 % 73.74 % 57.55 % 164 39.73 %

Benchmark MT rate PT rate ML rate FRAG
CAR 70.92 % 23.69 % 5.38 % 421
PEDESTRIAN 33.68 % 31.27 % 35.05 % 659

Benchmark # Dets # Tracks
CAR 31128 789
PEDESTRIAN 16841 304

This table as LaTeX

This figure as: png pdf

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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