Abstract
In an automated manner, In an automated manner, holistic traffic management is essential to enhance management in metro cities and even in two-tier cities. Detection of Vehicle flow is deemed to be crucial in the management of Traffic. In fact, flow of the Traffic shows the state of the Traffic in a definite amount and helps to rectify situations leading to traffic jam. Particularly this project intends to elucidate a traffic TV for non-chaotic vehicular traffic management. The fundamental idea includes five steps: subtraction of background, detection of the blob, blob analysis, pursuit of blob and reckoning of vehicle. Ideally, a vehicle is considered as associate rectangular patch and classified via blob analysis. After analyzing the blob of vehicles, the pertinent choices unit of mensuration extracted. The pursuit of moving targets is achieved by examination the extracted choices and activity. The experimental results show that the projected system can give a huge amount of useful information for traffic investigation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mu G, Xinyu Z, Deyi L, Tianlei Z, Lifeng A (2015) Traffic light detection and recognition for autonomous vehicles 22(1):50–56
Shu-Chung Y-C, Chang C-H (2015) A lane detection approach based on intelligent vision. Comput Electr Eng 42:23–29
Alcantarilla PF, Sotelo MA, Bergasa LM (2008) Automatic daytime road traffic control and monitoring system
Hussian R (2013) WSN applications: automated ıntelligent traffic control system using sensors. J IJSC E 3(3)
Sinhmar P (2012) Intelligent traffic light and density control using Ir sensors and microcontroller 2(2)
Teodorescu H et al (1999) Fuzzy and neuro-fuzzy systems in medicine. CRC Press, Boca Raton, FL, USA
Danescu R, Nedevschi S, Meinecke M, Graf T (2007) Stereo vision based vehicle tracking in urban traffic environments. In: IEEE conference on ıntelligent transportation system, pp 400–404
Ferrier NJ, Rowe SM, Blake A (1994) Real-time traffic monitoring. In: Proceeding of the second IEEE workshop on applications of computer vision, pp 81–88
Vu A, Barth M (2019) Catadiopetric omnidirectional vision system integration for vehicle-based sensing. In: Proceeding of IEEE ıntelligent transportation system conference
Cao M, Vu A, Barth M. A novel omni-directional vision sensing technique for traffic surveillance. In: Proceeding of IEEE
Jin B, Zou D, Gan Y (2010) Research and design of traffic detection based on GPRS. In: IEEE conference on advanced computer control (ICACC)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Darshane, S.V., Kagade, R.B., B.Thigale, S. (2021). Involuntary Traffic Control System. In: Pawar, P.M., Balasubramaniam, R., Ronge, B.P., Salunkhe, S.B., Vibhute, A.S., Melinamath, B. (eds) Techno-Societal 2020. Springer, Cham. https://doi.org/10.1007/978-3-030-69921-5_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-69921-5_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69920-8
Online ISBN: 978-3-030-69921-5
eBook Packages: EngineeringEngineering (R0)