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 commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 38.33 % 78.41 % 46.22 % 85.23 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 47.43 % 99.58 % 64.25 % 16622 70 18425 0.63 % 17493 458

Benchmark MT PT ML IDS FRAG
CAR 18.00 % 45.85 % 36.15 % 2716 3225

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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