4/4/2023 0 Comments Video js resolution switcher![]() Gao Y, Lee H (2015) Vehicle make recognition based on convolutional neural network. ![]() Wang X, Zhang W, Wu X, Xiao L, Qian Y, Fang Z (2017) Real-time vehicle type classification with deep convolutional neural networks. Int J Adv Robot Syst 1–12ĭong Z, Wu Y, Pei M, Jia Y (2015) Vehicle type classification using a semisupervised convolutional neural network. Kul S, Eken S, Sayar A (2017) Distributed and collaborative real-time vehicle detection and classification over the video streams. 2001 IEEE intelligent transportation systems Garibotto G, Castello P, Del Ninno E, Pedrazzi P, Zan G (2001) Speed- vision: speed measurement by license plate reading and tracking. Sridharamurthy K, Pernaje Govinda A, Gopala JD, Varaprasad G (2016) Violation detection method for vehicular ad hoc networking 9(3):201–207 In: 2011 14th international IEEE conference on intelligent transportation systems (ITSC) Evolut Intell 13(1):83–91 SpringerĪliane N, Fernandez J, Bemposta S, Mata M (2011) Traffic violation alert and management. Şentaş A (2020) Performance evaluation of support vector machine and convolutional neural network algorithms in real-time vehicle type and color classification. In: 39th international conference information systems architecture and technology, ISAT 2018, vol 853. Satılmış Y, Tufan F, Şara M, Karslı M (2018) CNN based traffic sign recognition for mini autonomous vehicles. In: Proceedings of the 12th IAPR international conference on pattern recognition. Ojala T, Pietikäinen M, Harwood D (1994) Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In: Proceedings of computer vision-ECCV, vol 110, pp 404–417 Int J Comput Vis 60(2):91–110īay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. In: Proceedings of IEEE computer society conferences computer vision and pattern recognition. IEEE, Malatya, pp 1–6ĭalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of international conference on artificial intelligence and data processing. Kul S, Eken S, Sayar A (2016) Measuring the efficiencies of vehicle classification algorithms on traffic surveillance video. O’Shea K, Nash R (2015) An introduction to convolutional neural networks With the help of the Bing Maps API, the shortest path distance between any two cameras is determined. The distance between any two cameras is the final factor used to compute average speed. When a vehicle enters the field of vision of a camera, the camera records the vehicle’s time and speed. The data provided by the cameras capturing the same vehicle are used to calculate the average speed. The vehicle is identified by its license plate number, and image processing determines its speed. The average speed of the vehicle is determined in this study utilizing the videos and pictures taken by the Electronic Detection System (EDS) installed at various points along the route of a vehicle. ![]() ![]() Today, radar equipment and camera speed restrictions are widely employed to prevent accidents brought on by speed-related infractions. To stop driver infractions, numerous safety measures have been implemented. The driver’s speed is one of the main contributors to these collisions since it is inappropriate for the traffic and road conditions. Traffic accidents have dramatically grown in recent years as a result of an increase in the number of vehicles. ![]()
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