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Saliency guided 2D-object annotation for instrumented vehicles

Gurram Munirathnam, Venkatesh ORCID: 0000-0002-4393-9267, Hu, Feiyan ORCID: 0000-0001-7451-6438, O'Connor, Noel E. ORCID: 0000-0002-4033-9135, Smeaton, Alan F. ORCID: 0000-0003-1028-8389, Yang, Zhen and Little, Suzanne ORCID: 0000-0003-3281-3471 (2019) Saliency guided 2D-object annotation for instrumented vehicles. In: 2019 International Conference on Content-Based Multimedia Indexing (CBMI 2019), 4 - 6 Sept 2019, Dublin, Ireland. ISBN 978-1-7281-4673-7

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Abstract

Instrumented vehicles can produce huge volumes of video data per vehicle per day that must be analysed automatically, often in real time. This analysis should include identifying the presence of objects and tagging these as semantic concepts such as car, pedestrian, etc. An important element in achieving this is the annotation of training data for machine learning algorithms, which requires accurate labels at a high-level of granularity. Current practise is to use trained human annotators who can annotate only a limited volume of video per day. In this paper, we demonstrate how a generic human saliency classifier can provide visual cues for object detection using deep learning approaches. Our work is applied to datasets for autonomous driving. Our experiments show that utilizing visual saliency improves the detection of small objects and increases the overall accuracy compared with a standalone single shot multibox detector.

Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:deep learning; visual saliency; object detection; data annotation; autonomous vehicles
Subjects:Computer Science > Algorithms
Computer Science > Artificial intelligence
Computer Science > Image processing
Computer Science > Machine learning
Computer Science > Multimedia systems
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: 2019 International Conference on Content-Based Multimedia Indexing. . IEEE. ISBN 978-1-7281-4673-7
Publisher:IEEE
Official URL:http://dx.doi.org/10.1109/CBMI.2019.8877460
Copyright Information:©2019 IEEE
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Huawei HIRP, The Insight Centre for Data Analytics is supported by Science Foundation Ireland under Grant Number SFI/12/RC/2289
ID Code:23595
Deposited On:24 Jul 2019 15:45 by Feiyan Hu . Last Modified 25 Jan 2021 14:38

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