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The data conundrum : compression of automotive imaging data and deep neural network based perception
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Chan, Pak Hung, Souvalioti, Georgina, Huggett, Anthony, Kirsch, Graham and Donzella, Valentina (2021) The data conundrum : compression of automotive imaging data and deep neural network based perception. In: The London Imaging Meeting (LIM). Published in: Proceedings Society for Imaging Science and Technology London Imaging Meeting 2021, 2021 (1). pp. 78-82. ISBN 0892083466. doi:10.2352/issn.2694-118X.2021.LIM-78 ISSN 2694-118X.
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Official URL: http://dx.doi.org/10.2352/issn.2694-118X.2021.LIM-...
Abstract
Video compression in automated vehicles and advanced driving assistance systems is of utmost importance to deal with the challenge of transmitting and processing the vast amount of video data generated per second by the sensor suite which is needed to support robust situational awareness. The objective of this paper is to demonstrate that video compression can be optimised based on the perception system that will utilise the data. We have considered the deployment of deep neural networks to implement object (i.e. vehicle) detection based on compressed video camera data extracted from the KITTI MoSeg dataset. Preliminary results indicate that re-training the neural network with M-JPEG compressed videos can improve the detection performance with compressed and uncompressed transmitted data, improving recalls and precision by up to 4% with respect to re-training with uncompressed data.
| Item Type: | Conference Item (Paper) | ||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
| Library of Congress Subject Headings (LCSH): | Driver assistance systems, Imaging systems, Automated vehicles, Neural networks (Computer science), Computer vision | ||||||
| Journal or Publication Title: | Proceedings Society for Imaging Science and Technology London Imaging Meeting 2021 | ||||||
| Publisher: | LIM | ||||||
| ISBN: | 0892083466 | ||||||
| ISSN: | 2694-118X | ||||||
| Official Date: | 20 September 2021 | ||||||
| Dates: |
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| Volume: | 2021 | ||||||
| Number: | 1 | ||||||
| Page Range: | pp. 78-82 | ||||||
| DOI: | 10.2352/issn.2694-118X.2021.LIM-78 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 9 March 2022 | ||||||
| Date of first compliant Open Access: | 9 March 2022 | ||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | The London Imaging Meeting (LIM) | ||||||
| Type of Event: | Other |
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