Method

Combined Image- and World-Space Tracking in Traffic Scenes* [st] [on] [CIWT*]
https://github.com/aljosaosep/ciwt

Submitted on 12 Feb. 2017 20:16 by
Aljosa Osep (RWTH Aachen)

Running time:0.28 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We use 3D object proposals to localize detections
accurately in 3D space and utilize 3D information
in our coupled 2D-3D filter, combined with
hypothesize-and-select framework.

* Using Regionlet detections
Parameters:
N/A
Latex Bibtex:
@inproceedings{Osep17ICRA,
title={Combined Image- and World-Space Tracking
in Traffic Scenes},
author={Osep, Aljosa and Mehner, Wolfgang
and Mathias, Markus and Leibe, Bastian},
booktitle={ICRA},
year={2017}
}

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 54.90 % 60.57 % 49.99 % 64.13 % 78.77 % 51.98 % 82.33 % 81.87 %
PEDESTRIAN 33.93 % 34.00 % 34.07 % 36.35 % 67.44 % 36.34 % 70.76 % 75.96 %

Benchmark TP FP FN
CAR 27046 7346 955
PEDESTRIAN 11329 11821 1149

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 74.44 % 79.32 % 75.86 % 491 58.17 %
PEDESTRIAN 42.10 % 71.15 % 43.97 % 433 27.99 %

Benchmark MT rate PT rate ML rate FRAG
CAR 50.46 % 38.31 % 11.23 % 552
PEDESTRIAN 14.09 % 50.86 % 35.05 % 851

Benchmark # Dets # Tracks
CAR 28001 1105
PEDESTRIAN 12478 623

This table as LaTeX

This figure as: png pdf

This figure as: png pdf

[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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