Method

Linear Programming Learned with SSVM [LP-SSVM*]


Submitted on 1 Dec. 2015 05:39 by
Shaofei Wang (University of California, Irvine)

Running time:0.02 s
Environment:1 core @ 2.5 Ghz (Matlab + C/C++)

Method Description:
Retrained using updated ground truth.
Use Regionlet detetions 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 77.63 % 77.80 % 77.81 % 82.60 %
PEDESTRIAN 43.76 % 70.48 % 44.08 % 91.83 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 83.35 % 96.27 % 89.34 % 31997 1239 6393 11.14 % 37084 1105
PEDESTRIAN 53.97 % 84.94 % 66.01 % 12569 2228 10718 20.03 % 16844 437

Benchmark MT PT ML IDS FRAG
CAR 56.31 % 35.23 % 8.46 % 62 539
PEDESTRIAN 20.62 % 45.02 % 34.36 % 73 809

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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