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 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 70.86 % 82.65 % 72.15 % 86.26 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 81.02 % 92.95 % 86.57 % 30885 2344 7235 21.07 % 38918 937

Benchmark MT PT ML IDS FRAG
CAR 58.00 % 34.15 % 7.85 % 443 975

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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