NDLI: Analysis and monitoring of a high density traffic flow at T-intersection using statistical computer vision based approach

Content Provider	IEEE Xplore Digital Library
Author	Hashmi, M.F. Keskar, A.G.
Copyright Year	2012
Description	Author affiliation: Department of Electronics Engineering, Visvesvaraya National Institute of Technology, Nagpur, India (Hashmi, M.F.; Keskar, A.G.)
Abstract	A reliable traffic flow monitoring and traffic analysis approach using computer vision techniques has been proposed in this paper. The exponential increase in traffic density at urban intersections in the past few decades has raised precious and challenging demands to computer vision algorithms and technological solutions. The focus of this paper is to suggest a statistical based approach to determine the traffic parameters at heavily crowded urban intersections. The algorithm in addition to accurate tracking and counting of freeway traffic also offers high efficiency for determining vehicle count at a high traffic density T-intersection. The system uses Intel Open CV library for image processing. The implementation of algorithm is done using C++. The real time video sequence is obtained from a stationary camera placed atop a high building overlooking the particular T intersection. This paper suggests a dynamic method where each vehicle at a T intersection is passed through a number of detection zones and the final count of vehicles is derived from a statistical equation.
Starting Page	52
Ending Page	57
File Size	466446
Page Count	6
File Format	PDF
ISBN	9781467351171
ISSN	21647143
e-ISBN	9781467351195
DOI	10.1109/ISDA.2012.6416512
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-27
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Computer vision Vehicle detection Heuristic algorithms T-Intersection Open CV Real-time systems Detection zone Monitoring Vehicles
Content Type	Text
Resource Type	Article

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