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Vision Based Vehicle Detection Using Hybrid Algorithm


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1 Department of Computer Science, TMV University, Pune, India
     

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Moving vehicle detection remain very critical and thus intended for Video-based solution, comparing to other techniques and by considering the traffic video sequence recorded from a video camera, this paper presents a video-based solution applied with adaptive subtracted background technology in combination with virtual detector and blob tracking technologies. This paper provides Experimental results moving vehicle detection which is implemented in Visual C++ code with OpenCV, thus the proposed method used for detection.

Keywords

Computer Vision, GMM, ITS, Open CV, Vehicle Detection.
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  • S. Sivaraman and M. Trivedi,(2013)“Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behaviour analysis”, IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 4, pp. 1773-1795.
  • Y. Liu, B. Tian, S. Chen, F. Zhu, and K. Wang, (2013) “A survey of vision-based vehicle detection and tracking techniques in ITS”, IEEE International Conference on Vehicular Electronics and Safety (ICVES), pp. 72-77.
  • M. Lei, D. Lefloch, P. Gouton, and K. Madani, (2008) “A Video-Based Real-Time Vehicle Counting System Using Adaptive Background Method”, IEEE International Conference on Signal Image Technology and Internet Based Systems, pp. 523-528.
  • G.D. Sullivan, K.D. Baker, A.D.Worrall, C.I. Attwood, and P.M. Remagnino, (1997)“Model-based vehicle detection and classification using orthographic approximations”, Image and Vision Computing, vol. 15, no. 8, pp. 649-654,
  • S. Gupte, O. Masoud, R.F.K. Martin, and N.P. Papanikolopoulos,(2002)“Detection and classification of vehicles”, IEEE Transactions on Intelligent Transportation Systems, vol. 3, no. 1, pp. 37-47.
  • S.C. Lee and R. Nevatia, “c”(2008), in Multimodal Technologies for Perception of Humans, Lecture Notes in Computer Science, vol. 4625, pp: 197-202.
  • N. Otsu,(1979) “A threshold selection method from gray-level histograms”, IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62-66.
  • P.S. Liao, T.S. Chen, and P.C. Chung,(2001) “A Fast Algorithm for Multilevel Thresholding”, Journal of Information Science and Engineering, vol. 17, no. 5, pp. 713-727.
  • B. Sun, S. Li,(2010) “Moving Cast Shadow Detection of Vehicle Using Combined Color Models”, Chinese Conference on Pattern Recognition (CCPR), pp. 1-5.
  • A. Sanin, C. Sanderson, and B.C. Lovell,(2012) “Shadow detection: A survey and comparative evaluation of recent methods”, Pattern Recognition, vol. 45, no. 4, pp. 1684-1695.
  • Bin-Feng Lin; Yi-Ming Chan; Li-Chen Fu; Pei-Yung Hsiao; Li-An Chuang; Shin-Shinh Huang; Min-Fang Lo;(2012)”Integrating Appearance and Edge Features for Sedan Vehicle Detection in the Blind-Spot Area,” Intelligent Transportation Systems, IEEETransactions on , vol.13, no.2, pp.737,747.
  • Feris, R.S.; Siddiquie, B.; Petterson, J.; Yun Zhai; Datta, A.; Brown, L.M.; Pankanti, S.,(2012) “Large-Scale Vehicle Detection,Indexing, and Search in Urban SurveillanceVideos,” Multimedia, IEEE Transactions on , vol.14, no.1,pp.28,42.
  • SooTeoh and Thomas Bräunl,(2012)”A reliability point and Kalmanfilter-based vehicle tracking technique”, Proceedings of the International Conference on Intelligent Systems (ICIS’2012), Penang, Malaysia, pp. 134-138.
  • Mithun, N.C.; Rashid, N.U.;Rahman, S.M.M.,(2012) “Detection and Classification of Vehicles from Video Using Multiple Time- Spatial Images,” Intelligent Transportation Systems, IEEE Transactions on, vol.13, no.3, pp.1215, 1225.
  • Chieh-Ling Huang;Heng-Ning Ma,(2012)”A Moving Object Detection Algorithm for Vehicle Localization,” Genetic andEvolutionary Computing (ICGEC), 2012 Sixth International Conference on , vol., no., pp.376,379, 25-28.
  • Morris, B.T.; Cuong Tran; Scora, G.; Trivedi, M.M.; Barth,M.J.,(2012) “Real-Time Video-Based Traffic Measurement and Visualization System for Energy/Emissions,” Intelligent Transportation Systems, IEEE Transactions on , vol.13, no.4,pp.1667,1678.

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  • Vision Based Vehicle Detection Using Hybrid Algorithm

Abstract Views: 376  |  PDF Views: 5

Authors

Padma Mishra
Department of Computer Science, TMV University, Pune, India
Anup Girdhar
Department of Computer Science, TMV University, Pune, India

Abstract


Moving vehicle detection remain very critical and thus intended for Video-based solution, comparing to other techniques and by considering the traffic video sequence recorded from a video camera, this paper presents a video-based solution applied with adaptive subtracted background technology in combination with virtual detector and blob tracking technologies. This paper provides Experimental results moving vehicle detection which is implemented in Visual C++ code with OpenCV, thus the proposed method used for detection.

Keywords


Computer Vision, GMM, ITS, Open CV, Vehicle Detection.

References





DOI: https://doi.org/10.25089/MERI%2F2017%2Fv11%2Fi1%2F164017