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 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 77.08 % 78.79 % 77.44 % 83.53 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 81.46 % 97.59 % 88.80 % 30747 760 6998 6.83 % 34422 1180

Benchmark MT PT ML IDS FRAG
CAR 51.38 % 39.69 % 8.92 % 123 713

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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