Measurement of Road Traffic Parameters Based on Multi-Vehicle Tracking

Development of computing power and cheap video cameras enabled today's traffic management systems to include more cameras and computer vision applications for transportation system monitoring and control. Combined with image processing algorithms cameras are used as sensors to measure road traffic parameters like flow volume, origin-destination matrices, classify vehicles, etc. In this paper we propose a system for measurement of road traffic parameters (basic motion model parameters and macro-scopic traffic parameters). The system is based on Local Binary Pattern (LBP) image features classification with a cascade of Gentle Adaboost (GAB) classifiers to determine vehicle existence and its location in an image. Additionally, vehicle tracking and counting in a road traffic video is performed by using Extended Kalman Filter (EKF) and virtual markers. The newly proposed system is compared with a system based on background subtraction. Comparison is performed by the means of execution time and accuracy.

[1]  Martin Glavin,et al.  Trends Towards Automotive Electronic Vision Systems for Mitigation of Accidents in Safety Critical Situations , 2011 .

[2]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[3]  Li Bai,et al.  Multiple Kernel Learning for vehicle detection in wide area motion imagery , 2012, 2012 15th International Conference on Information Fusion.

[4]  Justin Domke,et al.  Gabor Filter Visualization , 2005 .

[5]  Shengcai Liao,et al.  Learning Multi-scale Block Local Binary Patterns for Face Recognition , 2007, ICB.

[6]  Atsushi Shimada,et al.  A fast algorithm for adaptive background model construction using parzen density estimation , 2007, 2007 IEEE Conference on Advanced Video and Signal Based Surveillance.

[7]  Sharath Pankanti,et al.  Learning Detectors from Large Datasets for Object Retrieval in Video Surveillance , 2012, 2012 IEEE International Conference on Multimedia and Expo.

[8]  Edouard Ivanjko,et al.  Real Time Vehicle Trajectory Estimation on Multiple Lanes , 2014 .

[9]  Oguzhan Urhan,et al.  Local Binary Pattern Based Fast Digital Image Stabilization , 2015, IEEE Signal Processing Letters.

[10]  Jiri Matas,et al.  A system for real-time detection and tracking of vehicles from a single car-mounted camera , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[11]  K. Kiratiratanapruk,et al.  Vehicle Detection and Tracking for Traffic Monitoring System , 2006, TENCON 2006 - 2006 IEEE Region 10 Conference.

[12]  Bo Li,et al.  Vehicle detection based on the deformable hybrid image template , 2013, Proceedings of 2013 IEEE International Conference on Vehicular Electronics and Safety.

[13]  Cheng-Yen Lin,et al.  Design of vehicle detection methods with OpenCL programming on multi-core systems , 2013, The 11th IEEE Symposium on Embedded Systems for Real-time Multimedia.

[14]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[15]  Xudong Jiang,et al.  Feature extraction for image recognition and computer vision , 2009, 2009 2nd IEEE International Conference on Computer Science and Information Technology.