S-TVDS: Smart Traffic Violation Detection System for Indian Traffic Scenario
Aman Kumar1, Shakti Kundu2, Santosh Kumar3, Umesh Kumar Tiwari4, Jasmeet Kal5
1Aman Kumar, CCSIT, Teerthanker Mahaveer University, Moradabad, India.
2Shakti Kundu, CCSIT, Teerthanker Mahaveer University, Moradabad, India.
3Santosh Kumar, Graphic Era Deemed University, Dehradun, India.
4Umesh Kumar Tiwari, Graphic Era Deemed University, Dehradun, India.
5Jasmeet Kalra, Graphic Era Hill University, Dehradun, India.
Manuscript received on 01 June 2019 | Revised Manuscript received on 07 June 2019 | Manuscript Published on 04 July 2020 | PP: 6-10 | Volume-8 Issue- 4S3 March 2019 | Retrieval Number: D10020384S319/2019©BEIESP | DOI: 10.35940/ijitee.D1002.0384S319
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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: The world’s second-largest road network is in India, which is directly and similarly proportional to the causes of road rules violations, accidents, and a large per-year death ratio. Now in semi-structured cities, it becomes the biggest challenge to make people abide by traffic rules. Much different automation has been proposed to automate and to make it happens in India. Many researchers are also trying to solve this with computing technology advancement. As from the recent past, AI & ML not only making things smarter but also have proven to a valuable technological human assistant of dealing with such issues with intelligence. In this paper, we proposed a smart traffic violation detection system as a solution for the same issues in the Indian scenario. The advanced and intelligent form of visual computing will assist in detection as well as pruning actions /alerts accordingly with classification of types of violations.
Keywords: Traffic Violation Detection, Smart Traffic Violation Detection & Alert System, AI Taffic Monitoring, Smart Traffic Management, Smart Traffic Alert System.
Scope of the Article: Knowledge Modelling, Integration, Transformation, and Management