Andreas Geiger (MPI Tübingen) | Philip Lenz (KIT) | Christoph Stiller (KIT) | Raquel Urtasun (University of Toronto)

Method

FANTrack: 3D Multi-Object Tracking with Feature Association Network [on] [at] [la] [FANTrack]
https://git.uwaterloo.ca/wise-lab/fantrack

Submitted on 6 Jun. 2019 05:42 by
Venkateshwaran Balasubramanian (WISE Lab - University of Waterloo)

Running time:0.04 s
Environment:8 cores @ >3.5 Ghz (Python)

Method Description:
Our approach uses a learning-based data association
framework in a tracking-by-detection setting. We
use a Siamese based feature extractor to generate
local similarity maps and feed them as input to a
CNN to solve the data association problem.
Parameters:
Detection Threshold = 0.28
Latex Bibtex:
@article{Baser2019FANTrack3M,
title={FANTrack: 3D Multi-Object Tracking with
Feature Association Network},
author={Erkan Baser and Venkateshwaran
Balasubramanian and Prarthana Bhattacharyya and
Krzysztof Czarnecki},
journal={ArXiv},
year={2019},
volume={abs/1905.02843}
}

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 77.72 % 82.33 % 78.16 % 85.85 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 83.66 % 96.15 % 89.47 % 31916 1277 6234 11.48 % 37711 2424

Benchmark MT PT ML IDS FRAG
CAR 62.62 % 28.62 % 8.77 % 150 812

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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