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 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 56.62 % 61.02 % 52.80 % 65.32 % 76.83 % 55.61 % 80.07 % 80.92 %
PEDESTRIAN 33.74 % 35.74 % 32.03 % 39.54 % 63.11 % 36.36 % 63.24 % 75.18 %

Benchmark TP FP FN
CAR 27991 6401 1247
PEDESTRIAN 12420 10730 2083

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 76.82 % 78.00 % 77.76 % 325 58.91 %
PEDESTRIAN 43.42 % 70.23 % 44.65 % 284 27.46 %

Benchmark MT rate PT rate ML rate FRAG
CAR 57.69 % 33.08 % 9.23 % 466
PEDESTRIAN 21.99 % 42.95 % 35.05 % 736

Benchmark # Dets # Tracks
CAR 29238 907
PEDESTRIAN 14503 383

This table as LaTeX