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Comparative Analysis and Detection of Street Parked Vehicles using Spatiotemporal Maps and Corner Detection Methods
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 136 - Number 6 |
| Year of Publication: 2016 |
| Authors: Manali Jayebhaye, M.R.Banwaskar |
10.5120/ijca2016908462
|
Manali Jayebhaye, M.R.Banwaskar . Comparative Analysis and Detection of Street Parked Vehicles using Spatiotemporal Maps and Corner Detection Methods. International Journal of Computer Applications. 136, 6 ( February 2016), 42-49. DOI=10.5120/ijca2016908462
Abstract
Traffic control and efficient use of existing infrastructure are key challenges in recent time. This work carries out comparative analysis of various corner detection methods in different conditions, to detect presence of parked vehicles in street lanes. Different algorithmic implementations are used, to detect corners over targets in video stream and then classify them over a spatio-temporal analysis maps to identify parked vehicles. The system has been evaluated using i-Lids public dataset and has proven to be robust against common difficulties found in Closed Circuit Television (CCTV) such as high noise levels, varying illumination and the presence of momentary occlusion by other vehicles. This could be used to detect illegal and double-parked vehicles in metropolitan areas and to detect incidents on roads with lanes. This paper implemented three corner detection algorithms, Harris, SIFT and FAST and analyzed the comparative performances of all three implementations over different parameters viz. precision, recall, false alarm and then conclude on best performing algorithm. The comparative results are presented.
References
- A. Albiol, L. Sanchis, A. albiol, and J. Mossi. Detection of parked vehicles using spatiotemporal maps. IEEE Transaction on Intelligent Transportation System, vol. 12, No. 4, 2011.
- L. Maddalena, and A. Petrosino. Self organizing and Fuzzy modeling for parked vehicles detection. ICAR-National Research Council.
- A. Bevilacqua and S. Vaccari. Real time detection of stopped vehicles in traffic scenes. In Proc. IEEE Conf. Adv. Video Signal Based Surveillance. pp. 266-270, 2007.
- A. Yilmaz, O. Javed, and M. Shah. Object tracking: A Survey ". ACM computer Surveillance, vol. 38, no. 4, pp. 1-45, Dec. 2006.
- P. Viola, and M. Jones. Rapid object detection using a Boosted cascade of simple features. Accepted Conference of Computer Vision and Pattern Recognition 2001.
- Z. Zhang, H. Lu, Xin Li, W. Li. and W. Yuan. Application of improves Harris algorithm in sub-pixel feature point extraction. IJCE, Vol. 6, No. 2, April 2014.
- Muthukrishnan R. and Ravi J. Image type- based assessment of SIFT and FAST algorithm. IJSP. Image Processing and pattern recognition vol. 8, no. 3, pp. 211-216, 2015.
- S. Borango, B. Boghossian, J. Black, D. Makris and S. Velastin. A DSP Based System for the Detection of Vehicles Parked In Prohibited Areas. In Processing IEEE Conference AVSS, Washington, DC, pp 260-265, 2007.
- J. T. Lee, M. Ryoo, M. Riley and J. Aggrawal. Real time illegal parking detection in outdoor environments using 1-D transformation. IEEE Trans. Circuis Sys. Video Technology, vol. 19, no. 7, pp. 1014-1024, 2009.
- R. Cucchiara, M. Piccardi. Vehicle Detection under Day and Night Illumination. Proceedings of 3rd International ICSC Symposium on Intelligent Industrial Automation (IIA 99).
- Mei H., A. Sethi, Y. Gong. A Detection Based Multiple Object Tracking Method. NEC Laboratories America, Cupertino, CA, USA.
- Konstantinos G. Derpanis. The Harris Corner Detector.
- i-LIDS dataset for AVSS 2007. [Online].Available: ftp://motinas.elec.qmul.ac.uk/pub/i-LIDS/.
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