Traffic sign and Obstacle detection in Vehicular Ad-Hoc Network (VANET) using HOG and Haar Classifier Techniques
T. Rajasekaran1, Sumathi Eswaran2, A.Rengarajan3, T.V.Ananthan4
1T.Rajesekaran is with the Research Scholar, Department of CS, Dr. M.G.R Educational and Research Institute, Chennai.
2Dr.Sumathi Eswaran is with the Dean-Department of CSE/IT, Dr. M.G.R Educational and Research Institute, Chennai.
3Dr.A.Rengarajan is with the Professor, Computer Science and Engineering, Vel Tech Multi tech Dr. RR Dr.SR Eng. College, Chennai
4Dr.T.V.Anaanthan is with the Professor, Department of CS, Dr. M.G.R Educational and Research Institute, Chennai.
Manuscript received on 02 July 2019 | Revised Manuscript received on 05 July 2019 | Manuscript published on 30 August 2019 | PP: 184-186 | Volume-8 Issue-10, August 2019 | Retrieval Number: H7141068819/2019©BEIESP | DOI: 10.35940/ijitee.H7141.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Wireless technology is progressing faster with time. People are doing research these days generally in the field of Wireless communication. VANET is the most developing exploration territory in remote correspondence. With the headway and development of the VANET, there will be an incredible upheaval in the field of telecommunication regarding quick handovers, arrange accessibility, security, wellbeing with the utilization of cutting edge applications and so on. VANET innovation is progressing with the progression of time however there are numerous issues that must be routed to make the system more overwhelming. In this paper we have proposed an effective mechanism for object detection in highways using Neural Networking and proving driver assistance to enhance the existing system to the next level as Intelligent Transportation System.
Keywords: Vehicular Ad-hoc Network (VANET), Intelligent Transportation System(ITS), Convolution Neural Networks(CNN), Object detection, HOG and Haar Classifier.
Scope of the Article: Ad-hoc Network