Method

Global Data Association for Multi-Object Tracking Using Network Flows [MCF]


Submitted on 25 Sep. 2013 18:15 by
Philip Lenz (KIT)

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (Python + C/C++)

Method Description:
We propose a network flow based optimization method for data association needed for multiple object tracking. The maximum-a-posteriori (MAP) data association problem is mapped into a cost-flow network with a non-overlap constraint on trajectories. The optimal data association is
found by a min-cost flow algorithm in the network.
Re-Implementation of this method without the Explicit Occlusion model.
Parameters:
None
Latex Bibtex:
@inproceedings{Zhang2008CVPR,
author = {Zhang, Li and Li, Yuan and Nevatia, Ramakant},
booktitle = {CVPR},
title = {Global data association for multi-object tracking using network flows.}
}

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 45.92 % 78.25 % 45.98 % 84.96 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 47.06 % 99.70 % 63.94 % 16473 49 18528 0.44 % 17348 1009

Benchmark MT PT ML IDS FRAG
CAR 14.92 % 47.85 % 37.23 % 21 581

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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