Method

LP-SSVM [LP-SSVM]


Submitted on 4 Dec. 2015 07:58 by
Shaofei Wang (University of California, Irvine)

Running time:0.05 s
Environment:1 core @ 2.5 Ghz (Matlab + C/C++)

Method Description:
Retrained using updated ground truth.
Use L-SVM detections available at:
http://www.cvlibs.net/datasets/kitti/eval_tracking.php
Detection time indicates seconds per frame to process
all three categories of objects (Car, Pedestrian and
Cyclist)
Parameters:
Latex Bibtex:
@article{Wang2016IJCV,
author = {S. Wang and C. Fowlkes},
title = {Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions},
issn = {1573-1405},
doi = {10.1007/s11263-016-0960-z},
journal = {International Journal of Computer Vision},
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 61.77 % 76.93 % 61.82 % 82.15 %
PEDESTRIAN 33.33 % 67.38 % 33.65 % 91.62 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 64.78 % 97.67 % 77.90 % 23144 551 12581 4.95 % 27061 895
PEDESTRIAN 42.98 % 82.44 % 56.51 % 9978 2126 13235 19.11 % 14418 410

Benchmark MT PT ML IDS FRAG
CAR 35.54 % 42.77 % 21.69 % 16 422
PEDESTRIAN 12.37 % 42.61 % 45.02 % 72 818

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.


eXTReMe Tracker