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 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 47.21 % 47.93 % 46.77 % 50.19 % 77.19 % 48.78 % 81.46 % 80.40 %
PEDESTRIAN 28.19 % 29.29 % 27.57 % 31.61 % 60.77 % 31.12 % 61.78 % 72.49 %

Benchmark TP FP FN
CAR 21811 12581 551
PEDESTRIAN 9910 13240 2131

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 61.08 % 77.10 % 61.82 % 253 46.56 %
PEDESTRIAN 32.42 % 67.05 % 33.60 % 273 18.32 %

Benchmark MT rate PT rate ML rate FRAG
CAR 35.69 % 42.62 % 21.69 % 415
PEDESTRIAN 13.75 % 41.58 % 44.67 % 841

Benchmark # Dets # Tracks
CAR 22362 753
PEDESTRIAN 12041 357

This table as LaTeX