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AUTOMATED VEHICLE SPEED MONITORING SYSTEM

Authors:

B.RUPADEVI¹, REPANA VINODKUMAR²

¹Associate Professor, Dept of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, AP, India,    Email:rupadevi.aitt@annamacharyagroup.org

²Post Graduate, Dept of MCA, Annamacharya Institute of Technology & Sciences, Tirupati, AP, India,

Email:repanavinodkumar54@gmail.com

Abstract— The positive impact of video and image processing in traffic monitoring, analysis, and traffic condition monitoring in various cities and urban vicinities cannot be overstated. This paper is another approach considering estimating vehicles' speed. Basically, traffic videos are collected from a stationary camera mounted on freeways. On the one hand, camera calibration with respect to accurate measure could mainly solve the geometrical equations conveniently supported directly using references. Camera calibration could yield precise measurements, but rather difficult in achieving the accurate speed. The system is also adaptable to extend into other traffic-related applications. The average errors in detected vehicle speed were ± 7 km/h, and the experiments were conducted under various resolutions and different video sequences. The accelerating advancement of computing power of commonplace computers has now enabled a broader reach in the integration of deep learning techniques in traffic surveillance video comparisons. It constitutes the core functions of traffic analysis, such as prediction of traffic flows, anomaly detection, vehicle re-identification, and vehicle tracking. Among these applications, traffic flow prediction, or vehicle speed estimation, is one of the most serious research subjects during the recent years. It can be a good solution to this problem in preventing road accidents and enhancing road engineering by almost checking the traffic demand. Vehicle speed prediction is effectively proposed through integration of state-of-the-art deep learning models and classical computer vision techniques. Some present-and-state-of-the-art efforts concerning estimation of vehicle speeds, detection of vehicles, and tracking of objects are discussed in this article. Optical flow contains the speed and direction information of pixel displacements in an image. Our final approach deals with collecting multi-scale convolutional network. This extracts parameter data of vehicles.

Keywords: Speed detection in vehicles, video sequences, computer vision, background modeling, traffic monitoring, OpenCV