Speed checker and reporting system
Keywords:
tracking, classifier, vehicle detection, speed estimationAbstract
Traffic flow prediction as well as automobile speed estimation is one of the most crucial study topics of current years. The quick current innovations in calculation ability of daily computer systems have made it possible to commonly apply deep learning approaches to the analysis of website traffic security video clips. Traffic flow prediction anomaly discovery, car re-identification, and also vehicle tracking are the standard parts of web traffic evaluation. Great services to this trouble might protect against web traffic crashes and also aid improves road preparation by better estimating transportation needs. In this project, we find the lorries and also track them in web traffic videos and approximate their speed. We follow the 'identity after that track' approach Artificial intelligence and also Computer system vision strategies are utilized for object tracking. An algorithm is used for creating a classifier, which is made use of for spotting objects. The vehicle activity is detected and also tracked along the structures making use of Idlib collection. It is based upon the relationship of pixels in bounding boxes having discovered items in successive structures. A data-driven method is made used to approximate the speed of the car.
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Shuai Hua, Manika Kapoor, David C. Anastasiu, "Vehicle Tracking and Speed Estimation from Traffic Videos", IEEE Trans. Department of Computer Engineering ,San Jose State University, San Jos, vol. 10 ,June 2018
Mahmoud Famouri, Zohreh Azimifar, "A Novel Motion Plane-Based Approach to Vehicle Speed Estimation",IEEE Trans. The Natural Sciences and Engineering Research Council of Canada, vol. 20 ,Nov 2019.
Ruimin Ke, Zhibin Li, Sung Kim, John Ash, Zhiyong Cui, and Yinhai Wang "Real-Time Bidirectional Traffic Flow Parameter Estimation
SukHwan Lim, John G. Apostolopoulos, Abbas El Gamal, "Optical Flow Estimation Using Temporally Oversampled Video" IEEE Trans. the Programmable Digital Camera Program by Agilent, Canon, Stanford University ,vol. 14, 2005.
Chong Chen , Dan Schonfeld, "A Particle Filtering Framework for Joint Video Tracking and Pose Estimation" IEEE Trans. Department of Electrical and Computer Engineering, University of Illinois at Chicago, Chicago, vol. 19, June 2010 .
Z. Sun, G. Bebis, R. Miller, in The International IEEE Conference on Intelligent Transportation Systems, 2004. Proceedings. On-road vehicle
M. Da Lio et al., “Artificial co-drivers as a universal enabling technology for future intelligent vehicles and transportation systems,” IEEE Trans. Intell. Transp. Syst., vol. 16, no. 1, pp. 244–263, Feb. 2015.
Jianping Wu , Zhaobin Liu , Caidong Gu , Maoxin Si , Fangyong Tan,”An algorithm for automatic vehicle speed detection using video camera”,Vol.3, 2009.
Fumio Yamazaki ,Wen Liu , T. Thuy Vu,”Vehicle Extraction and Speed Detection from Digital Aerial Images”,Vol.3,2008.
Jin-xiang Wang, “Research of vehicle speed detection algorithm in video surveillance”,Vol.4,2016.
Jozef Gerát ,Dominik Sopiak ,Miloš Oravec , Jarmila Pavlovicová, “Vehicle Speed Detection from camera stream using image processing methods”,Vol.6,2017.
Xiao-Feng Gu , Zi-Wei Chen , Ting-Song Ma , Fan Li , Long Yan,“Real-Time vehicle speed detection and tracking using deep neural networks”,Vol.6,2017.
M.D. Enjat Munajat , Dwi H. Widyantoro ,Rinaldi Munir, “Vehicle detection and tracking based on corner and lines adjacent detection features”, Vol.4,2016.
Panya Chanawangsa , Jingyan Wan ,Changxu Wu , Chang Wen Chen, “A novel 2D-3D hybrid approach to vehicle trajectory and speed estimation”, 2014.
Seongrae Kim , Junhee Lee , Youngmin Kim, “Speed-adaptive ratio-based lane detection algorithm for self-driving vehicles”,2016 International SoC Design Conference (ISOCC),2016.
K.V. Kiran Kumar ,Pallavi Chandrakant , Santosh Kumar , K.J. Kushal,” Vehicle Speed Detection Using Corner Detection”,2014 Fifth International Conference on Signal and Image Processing,Vol.5,2014.
Marcin L. Eichner , Toby P. Breckon, “Integrated speed limit detection and recognition from real-time video”,2008 IEEE Intelligent Vehicles Symposium,Vol.7,2018.
Praveen M Dhulavvagol , Abhilash Desai , Renuka Ganiger, “Vehical Tracking and Estimation of Moving Vehicle for Traffic Surveillance Applications”,2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC),2017.
Xiao-Feng Gu , Zi-Wei Chen , Ting-Song Ma , Fan Li , Long Yan,“Real-Time vehicle speed detection and tracking using deep neural networks”,Vol.6,2017.
Ruimin Ke, Zhibin Li, Sung Kim, John Ash, Zhiyong Cui, and Yinhai Wang "Real-Time Bidirectional Traffic Flow Parameter Estimation From Aerial Videos" IEEE Trans. National Natural Science Foundation of China , vol. 18, July 2017.
C. Stauffer and W. E. L. Grimson, Adaptive background mixture models for real-time tracking, in Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on, Fort Collins, 1999, p. 252 Vol. 2
PF. Felzenszwalb, RB. Girshick, D. McAllester, and D. Ramanan, Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell.
Dataset of traffic videos http://aiskyeye.com/
Dataset of images used for training classifier https://ai.stanford.edu/~jkrause/cars/car_dataset.html
Rinartha, K., & Suryasa, W. (2017). Comparative study for better result on query suggestion of article searching with MySQL pattern matching and Jaccard similarity. In 2017 5th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-4). IEEE.
Rinartha, K., Suryasa, W., & Kartika, L. G. S. (2018). Comparative Analysis of String Similarity on Dynamic Query Suggestions. In 2018 Electrical Power, Electronics, Communications, Controls and Informatics Seminar (EECCIS) (pp. 399-404). IEEE.
Dwijayanti, N., Mufdlilah, M., & Suryaningsih, E. K. (2022). The role of midwives in the application of classroom services for pregnant women during the COVID-19 pandemic period. International Journal of Health & Medical Sciences, 5(3). https://doi.org/10.21744/ijhms.v5n3.1918
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