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Video surveillance, detection, video classification, Gaussian Mixture Model, Bag of Features, Support Vector Machine
Traffic Analysis has been a problem that city planners have dealt with for years. Smarter ways are being developed to analyze traffic and streamline the process. Analysis of traffic may account for the number of vehicles in an area per some arbitrary time period and the class of vehicles. People have designed such mechanism for decades now but most of them involve use of sensors to detect the vehicles i.e. a couple of proximity sensors to calculate the direction of the moving vehicle and to keep the vehicle count. Even though over the time these systems have matured and are highly effective, they are not very budget friendly. The problem is such systems require maintenance and periodic calibration. Therefore, this study has purposed a vision based vehicle counting and classification system. The system involves capturing of frames from the video to perform background subtraction in order detect and count the vehicles using Gaussian Mixture Model (GMM) background subtraction then it classifies the vehicles by comparing the contour areas to the assumed values. The substantial contribution of the work is the comparison of two classification methods. Classification has been implemented using Contour Comparison (CC) as well as Bag of Features (BoF) and Support Vector Machine (SVM) method.
Sheeraz Memon, Sania Bhatti, Liaquat A. Thebo, Mir Muhammad B. Talpur, Mohsin A. Memon, " A Video based Vehicle Detection, Counting and Classification System", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.9, pp. 34-41, 2018. DOI: 10.5815/ijigsp.2018.09.05
S.-Y. Cheung, and P.P. Varaiya, “Traffic surveillance by wireless sensor networks: Final report”, PhD diss., University of California at Berkeley, 2006.
S. Oh, S. Ritchie, and C. Oh, “Real-time traffic measurement from single loop inductive signatures”, Transportation Research Record: Journal of the Transportation Research Board, (1804), pp. 98-106, 2002.
B. Coifman, “Vehicle level evaluation of loop detectors and the remote traffic microwave sensor”, Journal of transportation engineering, vol. 132, no.3, pp. 213-226, 2006.
M. Tursun, and G. Amrulla, “A video based real-time vehicle counting system using optimized virtual loop method”, IEEE 8th International workshop on Systems Signal Processing and their Applications (WoSSPA), 2013.
M. Lei, D. Lefloch, P. Gouton, K. Madani, “A video-based real-time vehicle counting system using adaptive background method”, IEEE International conference on Signal Image Technology and Internet Based Systems (SITIS'08), pp. 523-528, 2008.
N.C. Mithun, N.U. Rashid, and S.M. Rahman, “Detection and classification of vehicles from video using multiple time-spatial images”, IEEE Transactions on Intelligent Transportation Systems, vol 13, no 3, p. 1215-1225, 2012.
R.T. Collins, et al., “A system for video surveillance and monitoring”, VASM final Report, Robotics Institute, Carnegie Mellon University, 2000, pp.1-68.
G. Yang, “Video Vehicle Detection Based on Self-Adaptive Background Update”, Journal of Nanjing Institute of Technology (Natural Science Edition), vol 2, p. 13, 2012.
F. Liu, and H. Koenig, “A survey of video encryption algorithms”, computers & security, vol. 29, no 1, pp. 3-15, 2010.
E. Bas, A.M. Tekalp, and F.S. Salman, “Automatic vehicle counting from video for traffic flow analysis”, IEEE Intelligent Vehicles Symposium, 2007.
H. Rabiu, “Vehicle detection and classification for cluttered urban intersection”, International Journal of Computer Science, Engineering and Applications, vol 3, no 1, p. 37, 2013.
M. Seki, H. Fujiwara, and K. Sumi, “A robust background subtraction method for changing background”, Fifth IEEE Workshop on Applications of Computer Vision, 2000.
J.J. Gibson, “The perception of the visual world”, 1950.
K. Wu, et al., “Overview of video-based vehicle detection technologies”, 2011.
N. Friedman, and S. Russell, “Image segmentation in video sequences: A probabilistic approach”, Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence, 1997, Morgan Kaufmann Publishers Inc.
C. Stauffer, and W.E.L. Grimson, “Learning patterns of activity using real-time tracking”, IEEE Transactions on pattern analysis and machine intelligence, 2000. Vol 22, no 8, pp. 747-757, 2000.
C. Stauffer, and W.E.L. Grimson, “Adaptive background mixture models for real-time tracking”, IEEE Computer Society Conference Computer Vision and Pattern Recognition, 1999.
A. Elgammal, D. Harwood, and L. Davis, “Non-parametric model for background subtraction”, European conference on computer vision, Springer, 2000.
S. O'Hara, and B.A. Draper, “Introduction to the bag of features paradigm for image classification and retrieval”, arXiv preprint arXiv:1101.3354, 2011.
D.G. Lowe, “Object recognition from local scale-invariant features”, Computer vision, Seventh IEEE international conference, 1999.
D.G. Lowe, “Distinctive image features from scale-invariant keypoints”, International journal of computer vision, vol 60, no 2, pp. 91-110, 2004.
V. Ramakrishnan, A. K. Prabhavathy, and J. Devishree, “A survey on vehicle detection techniques in aerial surveillance”, International Journal of Computer Applications, vol 55, no 18, 2012.
B. Pawar, V.T.Humbe, L. Kundani, “Morphology Based Moving Vehicle Detection”. International Conference On Big Data Analytics and computational Intelligence (ICBDACI), pp. 217-223, 2017.
R.H. Pena-Gonzalez, M.A Nuno-Magada, “Computer vision based real-time vehicle tracking and classiﬁcation system”, IEEE 57th International Midwest Symposium on Circuits and systems (MWSCAS), pp. 679-682, 2014.
A. Suryatali, V.B. Dharmadhikari, “Computer Vision Based Vehicle Detection for Toll Collection System Using Embedded Linux”, International Conference on Circuit, Power and Computing Technologies (ICCPCT), pp. 1-7, 2015.
N. Seenouvong, U. Watchareeruetai, C. Nuthong, K. Khongsomboon, “A Computer Vision Based Vehicle Detection and Counting System”, IEEE 8th International conference on Knowledge and Smart Technology (KST), pp.224-227, 2016.
A.B. Godbehere, A. Matsukawa, and K. Goldberg, “Visual tracking of human visitors under variable-lighting conditions for a responsive audio art installation”, IEEE, American Control Conference (ACC), pp. 4305-4312, 2012.
P. KaewTraKulPong, and R. Bowden, “An improved adaptive background mixture model for real-time tracking with shadow detection”, Video-based surveillance systems, Springer. pp. 135-144, 2002.
Z. Zivkovic, “Improved adaptive Gaussian mixture model for background subtraction”, 17th International Conference on Pattern Recognition(ICPR), 2004.
Z. Zivkovic, and F. van der Heijden, “Efficient adaptive density estimation per image pixel for the task of background subtraction”, Pattern recognition letters, vol 27, no 7, pp. 773-780, 2006.
K. Robert, “Night-time traffic surveillance: A robust framework for multi-vehicle detection, classification and tracking”, Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS'09), pp. 1-6, 2009.