Method

Fast MO Extrapolation Tracker [on] [extraCK]


Submitted on 10 Jan. 2018 12:40 by
Gultekin Gunduz (Galatasaray University)

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

Method Description:
Motion feature extrapolation and affinity cost
weighted by appearance features. Tracklet
assignment is done by solving linear sum
assignment problem.
Parameters:
Object Detection Threshold 0.4,
Latex Bibtex:
@inproceedings{gunduz2018lightweight,
title={A lightweight online multiple object
vehicle tracking method},
author={Gunduz, Gultekin and
Acarman, Tankut},
booktitle={Intelligent Vehicles Symposium
(IV), 2018 IEEE},
pages={427--432},
year={2018},
organization={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 79.99 % 82.46 % 80.99 % 86.02 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 84.51 % 98.04 % 90.77 % 32156 642 5896 5.77 % 35250 1099

Benchmark MT PT ML IDS FRAG
CAR 62.15 % 32.31 % 5.54 % 343 938

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