Real-Time category-based and general obstacle detection for autonomous driving

Noa Garnett, Shai Silberstein, Shaul Oron, Ethan Fetaya, Uri Verner, Ariel Ayash, Vlad Goldner, Rafi Cohen, Kobi Horn, Dan Levi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

51 Scopus citations

Abstract

Detecting obstacles, both dynamic and static, with near-to-perfect accuracy and low latency, is a crucial enabler of autonomous driving. In recent years obstacle detection methods increasingly rely on cameras instead of Lidars. Camera-based obstacle detection is commonly solved by detecting instances of known categories. However, in many situations the vehicle faces un-categorized obstacles, both static and dynamic. Column-based general obstacle detection covers all 3D obstacles but does not provide object-instance classification, segmentation and motion prediction. In this paper we present a unified deep convolutional network combining these two complementary functions in one computationally efficient framework capable of realtime performance. Training the network uses both manually and automatically generated annotations using Lidar. In addition, we show several improvements to existing column-based obstacle detection, namely an improved network architecture, a new dataset and a major enhancement of the automatic ground truth algorithm.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages198-205
Number of pages8
ISBN (Electronic)9781538610343
DOIs
StatePublished - 1 Jul 2017
Externally publishedYes
Event16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 - Venice, Italy
Duration: 22 Oct 201729 Oct 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Volume2018-January

Conference

Conference16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
Country/TerritoryItaly
CityVenice
Period22/10/1729/10/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

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