Please use this identifier to cite or link to this item:
Title: A New Hybrid Architecture for Real-Time Detection of Emergency Vehicles
Authors: Jonnadula, Eshwar Prithvi
Khilar, Pabitra Mohan
Keywords: VANET
Emergency Vehicles
Image Processing
Machine Learning
Issue Date: Sep-2019
Citation: 4th International conference on computer vision & image processing (CVIP 2019), Jaipur, India, 27-29 September 2019
Abstract: VANET is a vital part of wireless networking. Vehicular movement is expanding indefinitely everywhere and is causing terrible problems to daily life. Almost all of the traffic lights now feature a fixed green light sequence and so green light sequence is determined without taking the existence of emergency vehicles into consideration. Consequently emergency vehicles such as ambulances, fire engines, police vehicles etc. are struck in traffic which might cause loss of valuable life and property. In this paper we present a new hybrid architecture for detection of emergency vehicles in real time. This hybrid architecture is based on the mixed features of image processing and machine learning. We also show the percentage decrease in the search space for the processing which results in faster detection of emergency vehicles.
Description: Copyright of this document belongs to proceedings publisher.
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2019_CVIP_PMKhilar_NewHybrid.pdfConference paper424.72 kBAdobe PDFView/Open    Request a copy

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.