Bootstrapped Learning for Car Detection in Planar Lidars

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00361168" target="_blank" >RIV/68407700:21230/22:00361168 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1145/3477314.3507312" target="_blank" >https://doi.org/10.1145/3477314.3507312</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3477314.3507312" target="_blank" >10.1145/3477314.3507312</a>

Result language

angličtina

Original language name

Bootstrapped Learning for Car Detection in Planar Lidars

Original language description

We present a proof-of-concept method for using bootstrapped learning for car detection in lidar scans using neural networks. We transfer knowledge from a traditional hand-engineered clustering and geometry-based detection technique to deep-learning-based methods. The geometry-based method automatically annotates laserscans from a vehicle travelling around a static car park over a long period of time. We use these annotations to automatically train the deep-learning neural network and evaluate and compare this method against the original geometrical method in various weather conditions. Furthermore, by using temporal filters, we can find situations where the original method was struggling or giving intermittent detections and still automatically annotate these frames and use them as part of the training process. Our evaluation indicates an increased detection accuracy and robustness as sensing conditions deteriorate compared to the method from which trained the neural network.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GC20-27034J" target="_blank" >GC20-27034J: Towards long-term autonomy through introduction of the temporal domain into spatial representations used in robotics</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing

ISBN

978-1-4503-8713-2

ISSN

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e-ISSN

—

Number of pages

8

Pages from-to

758-765

Publisher name

ACM

Place of publication

New York

Event location

Virtual

Event date

Apr 25, 2022

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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