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 HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.


Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA
CAR 43.06 % 44.75 % 41.70 % 46.22 % 80.08 % 43.11 % 84.22 % 81.84 %
PEDESTRIAN 27.80 % 27.41 % 28.61 % 29.38 % 62.30 % 30.44 % 68.62 % 73.55 %

Benchmark TP FP FN
CAR 19739 14653 109
PEDESTRIAN 9301 13849 1616

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 56.00 % 78.98 % 57.08 % 371 43.93 %
PEDESTRIAN 31.75 % 68.19 % 33.20 % 334 18.97 %

Benchmark MT rate PT rate ML rate FRAG
CAR 26.92 % 46.46 % 26.61 % 449
PEDESTRIAN 10.65 % 42.27 % 47.08 % 733

Benchmark # Dets # Tracks
CAR 19848 838
PEDESTRIAN 10917 507

This table as LaTeX

This figure as: png pdf

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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