Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Cooperative object classification for driving applications

Tools
- Tools
+ Tools

Arnold, Eduardo, Al-Jarrah, Omar Y., Dianati, Mehrdad, Fallah, Saber, Oxtoby, David and Mouzakitis, Alexandros (2019) Cooperative object classification for driving applications. In: IEEE Symposium on Intelligent Vehicle, Paris, France, 9-12 Jun 2019. Published in: 2019 IEEE Intelligent Vehicles Symposium (IV) pp. 2484-2489. doi:10.1109/IVS.2019.8813811 ISSN 2642-7214.

[img]
Preview
PDF
WRAP-Cooperative-object-classification-driving-applications-2021.pdf - Accepted Version - Requires a PDF viewer.

Download (1124Kb) | Preview
Official URL: http://dx.doi.org/10.1109/IVS.2019.8813811

Request Changes to record.

Abstract

3D object classification can be realised by rendering views of the same object from different angles and aggregating all the views to build a classifier. Although this approach has been previously proposed for general objects classification, most existing works did not consider visual impairments. In contrast, this paper considers the problem of 3D object classification for driving applications under impairments (e.g. occlusion and sensor noise) by generating an application-specific dataset. We present a cooperative object classification method where multiple images of the same object seen from different perspectives (agents) are exploited to generate more accurate classification. We consider model generalisation capability and its resilience to impairments. We introduce an occlusion model with higher resemblance to real-world occlusion and use a simplified sensor noise model. The experimental results show that the cooperative model, relying on multiple views, significantly outperforms single-view methods and is effective in mitigating the effects of occlusion and sensor noise.

Item Type: Conference Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
R Medicine > RE Ophthalmology
T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH): Three-dimensional modeling , Pattern recognition systems, Shapes -- Computer simulation, Vision disorders , Computer vision
Journal or Publication Title: 2019 IEEE Intelligent Vehicles Symposium (IV)
Publisher: IEEE
ISSN: 2642-7214
Book Title: 2019 IEEE Intelligent Vehicles Symposium (IV)
Official Date: 29 August 2019
Dates:
DateEvent
29 August 2019Published
11 April 2019Accepted
Page Range: pp. 2484-2489
DOI: 10.1109/IVS.2019.8813811
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date of first compliant deposit: 23 August 2021
Date of first compliant Open Access: 24 August 2021
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDJaguar Land Rover (Firm)http://viaf.org/viaf/305209406
EP/N01300X/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
Conference Paper Type: Paper
Title of Event: IEEE Symposium on Intelligent Vehicle
Type of Event: Conference
Location of Event: Paris, France
Date(s) of Event: 9-12 Jun 2019
Related URLs:
  • Related dataset

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us