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 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 56.09 % 60.70 % 52.15 % 64.97 % 77.83 % 54.87 % 81.17 % 81.94 %
PEDESTRIAN 34.66 % 34.31 % 35.21 % 36.70 % 67.74 % 38.33 % 69.46 % 76.23 %

Benchmark TP FP FN
CAR 27399 6993 1310
PEDESTRIAN 11398 11752 1144

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 74.92 % 79.46 % 75.86 % 324 58.55 %
PEDESTRIAN 43.26 % 71.57 % 44.29 % 239 29.26 %

Benchmark MT rate PT rate ML rate FRAG
CAR 53.69 % 34.00 % 12.31 % 317
PEDESTRIAN 17.87 % 38.49 % 43.64 % 491

Benchmark # Dets # Tracks
CAR 28709 855
PEDESTRIAN 12542 354

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.


eXTReMe Tracker