Method

Bayesian Multi-Object Tracking Using Motion Context from Multiple Objects [on] [RMOT]


Submitted on 26 Nov. 2014 11:27 by
Chang-Ryeol Lee (Gwangju Institue of Science and Technology)

Running time:0.01 s
Environment:1 core @ 3.5 Ghz (Matlab)

Method Description:
Parameters:
Latex Bibtex:
@inproceedings{
Yoon2015WACV,
author = "Ju Hong Yoon and Ming-Hsuan Yang and
Jongwoo
Lim and Kuk-Jin Yoon",
booktitle = "IEEE Winter Conference on
Applications of
Computer Vision (WACV)",
title = "Bayesian Multi-Object Tracking Using
Motion
Context from Multiple Objects",
year = "2015"
}

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 44.80 % 42.02 % 48.32 % 44.53 % 73.59 % 51.68 % 77.62 % 78.92 %
PEDESTRIAN 34.09 % 29.61 % 39.45 % 32.12 % 60.92 % 43.14 % 63.59 % 72.99 %

Benchmark TP FP FN
CAR 19445 14947 1367
PEDESTRIAN 10134 13016 2073

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 51.92 % 75.39 % 52.56 % 221 38.01 %
PEDESTRIAN 34.05 % 67.80 % 34.82 % 179 19.95 %

Benchmark MT rate PT rate ML rate FRAG
CAR 21.69 % 45.85 % 32.46 % 351
PEDESTRIAN 13.06 % 39.52 % 47.42 % 662

Benchmark # Dets # Tracks
CAR 20812 612
PEDESTRIAN 12207 261

This table as LaTeX