Review on Traffic Density Estimation Using Computer Vision Technique
Keywords:
Image Processing, Image Segmentation, CCTV camera, Traffic Density EstimationAbstract
The activity on the streets is builds step by step. The need of creating framework that can oversee and control the movement on street . The movement of numerous vehicle on streets is additionally critical for taking different choices identified with activity. The framework brings an activity picture from a CCTV camera to process in the framework as an information. From that point onward, the framework finds for movement clog and gets the outcomes in three rush hour gridlock conditions as Flow, Heavy, and Jammed. At long last, a client can utilize the framework for a transportation arranging or a convergence movement control. For execution, the framework utilizes a picture preparing system to dissect for a movement condition. It recognizes what number of items or autos out and about. And after that, the framework associates a movement condition result with a database for a transportation arranging.
References
- P. Mishra, M. Athiq, A. Nandoriya and S. Chaudhuri, “Video based Vehicle Detection and Classification in Traffic Conditions using a Novel Kernel Classifier” IETE journal of research vol 2013.
- C. Zhao, J. Wang, C. Xie and H. Lu,” A Coarse-to-fine logo recognition method in video streams” National Laboratory of Pattern Recognition, Casia, Beijing China.
- P.M. Daigavane and M.B. Daigavane,” Vehicle Detection and Neural Network Application for Vehicle Classification” International Conference on Computational Intelligence and Communication Systems 2011.
- Z. Chen and T. Ellis,” Multi-shape Descriptor Vehicle Classification for Urban Traffic” International Conference on Digital Image Computing: Techniques and Applications 2011S.
- Messelodi, M. Modena, and M. Zanin ,“A computer vision system for the detection and classification of vehicles at urban road intersections” Springer-Verlag 2005 London Limited.
- K. Deb and K. Nathr “Vehicle Detection Based on Video for Traffic Surveillance on road”. Int. JComp Sci. Emerging Tech Vol-3 No 4 August, 2012.
- NASA K. Dhanya, M. Manime kalai, B. Asmin and G. Vani, “Tracking and Identification of Multiple Vehicles”.
- A. Suryatali and V. B. Dharmadhikari, “Computer Vision Based Vehicle Detection for Toll Collection System Using Embedded Linux”. International Conference on Circuit, Power and Computing Technologies2015
- S. Kumar and N.Sivanandam,” Object Detection and Tracking in Video using particle filter “ICCCNT'12 IEEE-2012 Coimbatore, India.
- B. Changalasetty , A. Badawy, W. Ghribi, H. Ashwi ,A.Mohammed , A.Shehri , S. Thota and R.Medisetty, “Identification and Classification of Moving Vehicles on Road” Computer Engineeringand Intelligent Systems pp 2222-2863 Vol.4, No.8, 2013
- M. Shaoqing and L. Zhengguang, “Real-time Vehicle Classification Method for Multilane Roads” ICIEA 2009.
- M. Osman and A.Bakar, “Classification of Moving Objects Using Active Vision Computer Vision”Computer Vision, Video, and Image Processing Laboratory(CVVIP) IEEE 2005.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRST
This work is licensed under a Creative Commons Attribution 4.0 International License.
