Wheeler-Classified Vehicle Detection System using CCTV Cameras
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10.5120/15679-4437 |
Pratishtha Gupta, G N Purohit and Saroj Kumari. Article: Wheeler-Classified Vehicle Detection System using CCTV Cameras. International Journal of Computer Applications 89(11):35-42, March 2014. Full text available. BibTeX
@article{key:article,
author = {Pratishtha Gupta and G N Purohit and Saroj Kumari},
title = {Article: Wheeler-Classified Vehicle Detection System using CCTV Cameras},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {89},
number = {11},
pages = {35-42},
month = {March},
note = {Full text available}
}
Abstract
Wheeler-Classified Vehicle Detection System is the implementation to check whether vehicle is two-wheeler or four-wheeler from the ordinary traffic images captured through CCTV cameras installed at crossings along a particular road at an instant which is an important task in Intelligent Transportation Systems (ITS). The key contribution of this work are the introduction of methodology to reflect type of vehicle (two or four-wheeler) using MATLAB. Firstly, the region containing the vehicle is identified which is then cropped automatically from the traffic image and vehicle type is resulted using the approach of total number of corners obtained through corner detection of individual vehicle. If total number of corners exceeds the defined limit then output is reflected as four-wheeler otherwise two-wheeler.
References
- Bowman, M. , Debray, S. K. , and Peterson, L. L. 1993. Reasoning about naming systems. .
- Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
- Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
- Sannella, M. J. 1994 Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398. , University of Washington.
- Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
- Brown, L. D. , Hua, H. , and Gao, C. 2003. A widget framework for augmented interaction in SCAPE.
- Y. T. Yu, M. F. Lau, "A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions", Journal of Systems and Software, 2005, in press.
- Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender.