Method

Near-Online Multi-target Tracking (HM baseline) [on] [NOMT-HM*]


Submitted on 10 Nov. 2014 01:56 by
Wongun Choi (NEC Laboratories)

Running time:0.09 s
Environment:8 cores @ 2.5 Ghz (Matlab + C/C++)

Method Description:
* use Regionlet detetions available at:
http://www.xiaoyumu.com/s/data/kitti-tracking.zip
Parameters:
n/a
Latex Bibtex:
@article{Choi2015ICCV,
author = {Choi, Wongun},
title = {Near-Online Multi-target Tracking with
Aggregated Local Flow Descriptor
},
journal= {ICCV},
year={2015},
}

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 75.20 % 80.02 % 75.51 % 84.45 %
PEDESTRIAN 39.26 % 71.14 % 40.05 % 92.43 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 80.99 % 96.45 % 88.04 % 31011 1143 7280 10.28 % 34602 978
PEDESTRIAN 50.54 % 83.33 % 62.92 % 11773 2355 11523 21.17 % 15964 492

Benchmark MT PT ML IDS FRAG
CAR 50.00 % 36.46 % 13.54 % 105 351
PEDESTRIAN 21.31 % 36.77 % 41.92 % 184 863

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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