Method

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


Submitted on 18 Nov. 2022 21:28 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 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 90.88 % 85.57 % 91.43 % 88.41 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 93.83 % 98.50 % 96.11 % 36428 555 2394 4.99 % 46229 1431

Benchmark MT PT ML IDS FRAG
CAR 86.92 % 11.08 % 2.00 % 189 426

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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