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.
Latex Bibtex:
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 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 33.98 % 35.97 % 32.32 % 36.87 % 79.67 % 33.65 % 82.48 % 81.31 %

Benchmark TP FP FN
CAR 15864 18528 49

CAR 44.40 % 78.31 % 45.98 % 545 34.39 %

Benchmark MT rate PT rate ML rate FRAG
CAR 14.92 % 47.54 % 37.54 % 497

Benchmark # Dets # Tracks
CAR 15913 927

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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