Method

FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online MOT [FAMNet]


Submitted on 23 Jul. 2019 02:53 by
Peng Chu (Temple University)

Running time:1.5 s
Environment:GPU @ 1.0 Ghz (Python)

Method Description:
In this paper, we present an end-to-end model, named FAMNet, where Feature extraction, Affinity
estimation and Multi-dimensional assignment are refined in a single network for online MOT.
Parameters:
K=2
Latex Bibtex:
@inproceedings{chufamnet,
title={FAMNet: Joint Learning of Feature,
Affinity and Multi-dimensional Assignment for
Online Multiple Object Tracking},
author={Chu, Peng and Ling, Haibin},
booktitle={ICCV},
year={2019}
}

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 52.56 % 61.00 % 45.51 % 64.40 % 78.67 % 48.66 % 77.41 % 81.47 %

Benchmark TP FP FN
CAR 27392 7000 762

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 75.92 % 78.78 % 77.43 % 521 59.02 %

Benchmark MT rate PT rate ML rate FRAG
CAR 52.46 % 37.85 % 9.69 % 614

Benchmark # Dets # Tracks
CAR 28154 953

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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