Method

Joint Constraints in Spatial and Temporal Domain [on] [JCSTD]


Submitted on 27 Apr. 2017 12:38 by
Tian Wei (MRT)

Running time:0.07 s
Environment:1 core @ 2.7 Ghz (C++)

Method Description:
Joint Constraints in Spatial and Temporal Domain
Parameters:
1 core
Latex Bibtex:
@ARTICLE{Tian2019MOT,
author={Wei Tian and Martin Lauer and Long Chen},
journal={IEEE Transactions on Intelligent
Transportation Systems},
title={Online Multi-Object Tracking Using Joint
Domain Information in Traffic Scenarios},
year={2019},
volume={},
number={},
pages={1-11},
doi={10.1109/TITS.2019.2892413},
ISSN={1524-9050},
month={Jan.},
}

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 80.57 % 81.81 % 80.75 % 85.69 %
PEDESTRIAN 44.20 % 72.09 % 44.43 % 91.79 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 83.37 % 98.72 % 90.40 % 31162 405 6217 3.64 % 34918 798
PEDESTRIAN 48.52 % 92.70 % 63.70 % 11286 889 11975 7.99 % 13018 264

Benchmark MT PT ML IDS FRAG
CAR 56.77 % 35.85 % 7.38 % 61 643
PEDESTRIAN 16.49 % 49.83 % 33.68 % 53 917

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.


eXTReMe Tracker