Method

Near Online Multi-target Tracking [NOMT]


Submitted on 21 Mar. 2015 01:57 by
Wongun Choi (NEC Laboratories)

Running time:0.09 s
Environment:16 core @ 2.5 Ghz (C++)

Method Description:
The algorithm generate consistent trajectories
given a set of detections in each frame in a (near)
online fashion. The trajectories are discovered
with a temporal latency up to 20 frames. More
details on the method will be updated
as soon as possible.
Parameters:
Reference detections are used:
http://www.cvlibs.net/download.php?
file=data_tracking_det_2.zip
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 66.60 % 78.17 % 66.64 % 83.06 %
PEDESTRIAN 36.93 % 67.75 % 37.08 % 91.28 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 69.21 % 98.05 % 81.14 % 24686 492 10981 4.42 % 28775 745
PEDESTRIAN 46.96 % 82.91 % 59.96 % 10905 2248 12318 20.21 % 15795 284

Benchmark MT PT ML IDS FRAG
CAR 41.08 % 33.69 % 25.23 % 13 150
PEDESTRIAN 17.87 % 39.52 % 42.61 % 34 789

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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