Method

S3Track: Self-supervised Tracking with Soft Assignment Flow [S3Track]


Submitted on 24 Jan. 2024 16:54 by
Anonymous (Anonymous)

Running time:0.03 s
Environment:1 core @ 2.5 Ghz (Python)

Method Description:
TBD
Parameters:
None
Latex Bibtex:
@inproceedings{s3track,
title={S$^3$Track: Self-supervised Tracking with
Soft Assignment Flow},
author={Anonymous}
}

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 77.58 % 77.91 % 77.93 % 84.49 % 84.41 % 82.02 % 88.98 % 88.35 %

Benchmark TP FP FN
CAR 32536 1856 1891

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 88.38 % 87.06 % 89.11 % 251 76.13 %

Benchmark MT rate PT rate ML rate FRAG
CAR 86.31 % 12.00 % 1.69 % 301

Benchmark # Dets # Tracks
CAR 34427 900

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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