Method

Structural Constraint Event Aggregation [on] [SCEA]


Submitted on 17 Nov. 2015 03:15 by
Chang-Ryeol Lee (Gwangju Institue of Science and Technology)

Running time:0.05 s
Environment:1 core @ 4.0 Ghz (Matlab + C/C++)

Method Description:
* use L-SVM 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 57.03 % 78.84 % 57.08 % 84.17 %
PEDESTRIAN 33.13 % 68.45 % 33.20 % 91.83 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 58.63 % 99.48 % 73.78 % 20764 109 14653 0.98 % 22746 1009
PEDESTRIAN 40.32 % 85.27 % 54.75 % 9357 1616 13849 14.53 % 12618 602

Benchmark MT PT ML IDS FRAG
CAR 26.92 % 46.46 % 26.62 % 17 461
PEDESTRIAN 9.62 % 43.64 % 46.74 % 16 717

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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