Method

SASN-MCF_nano [SASN-MCF_nano]


Submitted on 31 Aug. 2019 22:06 by
Gultekin Gunduz (Galatasaray University)

Running time:0.02 s
Environment:1 core @ 3.0 Ghz (Python)

Method Description:
Parameters:
Latex Bibtex:
@article{gunduz2019efficient,
title={Efficient Multi-Object Tracking by Strong Associations on Temporal Window},
author={Gunduz, Gultekin and Acarman, Tankut},
journal={IEEE Transactions on Intelligent Vehicles},
year={2019},
publisher={IEEE}
}

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 52.24 % 59.65 % 46.22 % 66.28 % 77.27 % 56.20 % 68.77 % 84.56 %

Benchmark TP FP FN
CAR 27098 7294 2403

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 69.82 % 82.37 % 71.80 % 683 55.93 %

Benchmark MT rate PT rate ML rate FRAG
CAR 57.38 % 34.62 % 8.00 % 703

Benchmark # Dets # Tracks
CAR 29501 843

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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