Please use this identifier to cite or link to this item:
Title: Rapid technique to eliminate moving shadows for accurate vehicle detection
Authors: Garg, Kratika
Ramakrishnan, Nirmala
Prakash, Alok
Srikanthan, Thambipillai
Bhatt, Punit
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Garg, K., Ramakrishnan, N., Prakash, A., Srikanthan, T. & Bhatt, P. (2019). Rapid technique to eliminate moving shadows for accurate vehicle detection. IEEE Winter Conference on Applications of Computer Vision (WACV), 1970-1978.
Abstract: Elimination of moving shadows is an essential step to achieve accurate vehicle detection and localization in automated traffic surveillance systems that aim to detect vehicles on road scenes captured by surveillance cameras. However, this is still a challenging problem as existing pixel based methods miss parts of vehicles and region-based methods, while accurate, incur higher computations. In this paper, we propose a highly accurate yet low-complexity block-based moving shadow elimination technique, which can effectively deal with varying shadow conditions. A novel shadow elimination pipeline is proposed that employs computationally lean features to quickly classify distinct vehicles from shadows, and uses a more sophisticated interior edge feature only for classification of difficult scenarios. Extensive evaluations on freely available and self-collected datasets demonstrate that the proposed technique achieves higher accuracy than other state-of-the-art techniques in varying scenarios. Additionally, it also achieves over 20 times speedup on a low-cost embedded platform, Odroid XU-4, over a state-of-the-art technique that achieves comparable accuracy. Experimental results confirm the real-time capability of the proposed approach while achieving robustness to varying shadow scenarios.
ISBN: 9781728119755
DOI: 10.1109/WACV.2019.00214
Rights: © 2019 Institute of Electrical and Electronics Engineers (IEEE). All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

Page view(s)

Updated on Sep 19, 2021

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.