Method

Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects [DP-MCF]
http://people.csail.mit.edu/hpirsiav/

Submitted on 25 Sep. 2013 18:06 by
Philip Lenz (KIT)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (Matlab)

Method Description:
Tracking is formulated a minimum cost flow problem. This method shows that the global solution can be obtained with a greedy algorithm that sequentially instantiates tracks using shortest path computations on a flow network.
Parameters:
Default Parameters.
Latex Bibtex:
@inproceedings{
Pirsiavash2011CVPR,
author = "Pirsiavash, Hamed and Ramanan, Deva and Fowlkes, Charless C.",
booktitle = "IEEE conference on Computer Vision and Pattern Recognition (CVPR)",
title = "Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects",
year = "2011"
}

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 25.97 % 35.69 % 19.12 % 36.76 % 78.84 % 28.98 % 39.84 % 81.19 %

Benchmark TP FP FN
CAR 15967 18425 70

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 36.89 % 78.50 % 46.22 % 3209 26.91 %

Benchmark MT rate PT rate ML rate FRAG
CAR 17.85 % 45.69 % 36.46 % 1083

Benchmark # Dets # Tracks
CAR 16037 446

This table as LaTeX

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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