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http://dx.doi.org/10.9717/kmms.2016.19.2.418

Realtime Vehicle Tracking and Region Detection in Indoor Parking Lot for Intelligent Parking Control  

Yeon, Seungho (Department of Electrical Information Control Engineering, Hongik Univ.)
Kim, Jaemin (School of Electronic and Electrical Engineering, Hongik Univ.)
Publication Information
Journal of Korea Multimedia Society / v.19, no.2, 2016 , pp. 418-427 More about this Journal
Abstract
A smart parking management requires to track a vehicle in a indoor parking lot and to detect the place where the vehicle is parked. An advanced parking system watches all space of the parking lot with CCTV cameras. We can use these cameras for vehicles tracking and detection. In order to cover a wide area with a camera, a fisheye lens is used. In this case the shape and size of an moving vehicle vary much with distance and angle to the camera. This makes vehicle detection and tracking difficult. In addition to the fisheye lens, the vehicle headlights also makes vehicle detection and tracking difficult. This paper describes a method of realtime vehicle detection and tracking robust to the harsh situation described above. In each image frame, we update the region of a vehicle and estimate the vehicle movement. First we approximate the shape of a car with a quadrangle and estimate the four sides of the car using multiple histograms of oriented gradient. Second we create a template by applying a distance transform to the car region and estimate the motion of the car with a template matching method.
Keywords
Smart Parking Management; Vehicle Tracking; Indoor Parking Lot;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 O. Barnich, "Improved Adaptive Gaussian Mixture Model for Background Subtraction," IEEE International Conference on Pattern Recognition, Vol. 2, pp. 28-31, 2004.
2 Intelligent Parking Guidance and Automatic Vehicle Positioning System, https://www.youtube.com/watch?v=rtwW8e_H_HI (accessed Dec., 1, 2015).
3 Z. Zivkovic, "ViBe: A Universal Background Subtraction Algorithm for Video Sequences," IEEE Transactions on Image Processing, Vol. 20, No. 6, pp. 1709-1724, 2011.   DOI
4 S. Yeon and J. Kim, "Realtime Object Region Detection Robust to Vehicle Headlight," Journal of Korea Multimedia Society, Vol. 18, No. 2, pp. 138-148, 2015.   DOI
5 J. G. Allen, R.Y.D. Xu, and J.S. Jin, "Object Tracking Using CamShift Algorithm and Multiple Quantized Feature Spaces," Proceedings of the Pan-Sydney Area Workshop on Visual Information Processing, pp. 3-7, 2004.
6 D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based Object Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 5, pp. 564-577, 2003.   DOI
7 A. Elgammal, D. Harwood, and L. Davis, “Background and Foreground Modeling Using Nonparametric Kernel Density Estimation for Visual Surveillance,” Proceedings of the IEEE, Vol. 90, No. 7, pp. 1151-1163, 2002.   DOI
8 K. Zhang, L. Zhang, and M. Yang, "Real-Time Object Tracking via Online Discriminative Feature Selection," IEEE Transactions on Image Processing, Vol. 22, No. 12, pp. 4664-4667, 2013.   DOI
9 G. Borgefors, “Distance Transformations in Digital Images," Computer Vision, Graphics, and Image Processing, Vol. 34, No. 3, pp. 344-371, 1986.   DOI
10 S. Holzer, S. Hinterstoisser, S. Ilic, and Nassir Navab, "Distance Transform Templates for Object Detection and Pose Estimation," Proceeding of Computer Vision and Pattern Recognition, pp. 1177-1184, 2009.
11 S. Hare, A. Saffari, H. Philip, and S. Torr, "Struck: Structured Output Tracking with Kernels," International Conference on Computer Vision, pp. 263-270, 2011.