Method

Online Domain Adaptation for Multi-Object Tracking [on] [ODAMOT]


Submitted on 1 May. 2015 21:29 by
Eleonora Vig (Xerox Research Centre Europe)

Running time:1 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
Online Domain Adaptation for Multi-Object Tracking
Parameters:
Latex Bibtex:
@inproceedings{Gaidon2015BMVC,
author = {Gaidon, A. and Vig, E.},
booktitle={British Machine Vision Conference (BMVC)},
title = {{Online Domain Adaptation for Multi-Object Tracking}},
year = {2015}
}

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 37.05 % 46.53 % 30.07 % 49.91 % 73.20 % 32.46 % 78.19 % 79.26 %

Benchmark TP FP FN
CAR 22087 12305 1363

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 57.03 % 75.37 % 60.26 % 1110 41.21 %

Benchmark MT rate PT rate ML rate FRAG
CAR 27.54 % 56.92 % 15.54 % 1015

Benchmark # Dets # Tracks
CAR 23450 1637

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.


eXTReMe Tracker