Method

Structural Constraint Event Aggregation [on] [SCEA*]


Submitted on 10 Nov. 2015 13:06 by
Chang-Ryeol Lee (Gwangju Institue of Science and Technology)

Running time:0.06 s
Environment:1 core @ 4.0 Ghz (Matlab + C/C++)

Method Description:
* use Regionlet detetions available at:
http://www.cvlibs.net/datasets/kitti/eval_tracking
.php
Parameters:
Latex Bibtex:
@inproceedings{
Yoon2016CVPR,
author = "Ju Hong Yoon and Chang-Ryeol Lee and
Ming-Hsuan Yang and
Kuk-Jin Yoon",
booktitle = "IEEE International Conference on
Computer Vision and Pattern Recognition (CVPR)",
title = "Online Multi-object Tracking via
Structural Constraint Event Aggregation",
year = "2016"
}

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 75.58 % 79.39 % 75.88 % 83.76 %
PEDESTRIAN 43.91 % 71.86 % 44.15 % 92.71 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 81.76 % 96.00 % 88.31 % 31330 1306 6989 11.74 % 35495 1017
PEDESTRIAN 49.52 % 90.69 % 64.06 % 11522 1183 11746 10.63 % 13763 380

Benchmark MT PT ML IDS FRAG
CAR 53.08 % 35.38 % 11.54 % 104 448
PEDESTRIAN 16.15 % 40.55 % 43.30 % 56 641

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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