Real time adaptive background estimation and road segmentation for vehicle classification

Our goal is detecting vehicle, lane of moving and classify in three types heavy vehicle such as trunk — semi heavy such as mini bus and van and light vehicle such as cab in order to recognize offender that is in illicit area. In this paper, we will propose an algorithm for moving vehicle detection different from previous works, detecting and classification carried out at several frame, this algorithm first segments best near field area of road according to camera installed point by Hough transform and k-mean line clustering. Then, this area is divided into many small non overlapped blocks and saved as a codebook and then extracted background of this area by adaptive background estimation. Vehicles is detected in two step, first kernel detection, second detecting sets that has the most correlation to this kernel, we have background subtracting and morphological binary image segmentation for vehicle detection by define of sets of ones connected together in binary image and identifying clustered sets that depend on single vehicle Occlusion detection and elimination and define type of it by energy of clustered sets on day and distance backlights and distance between backlights and headlights at night and a neural network classification. Proposed algorithm has a satisfying performance under various conditions, which can robustly and effectively eliminate the influence of casting shadows, headlights, or bad illumination and occlusion.

[1]  A. Lai,et al.  A fast and accurate scoreboard algorithm for estimating stationary backgrounds in an image sequence , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

[2]  W. Eric L. Grimson,et al.  Edge-based rich representation for vehicle classification , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[3]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[4]  Paola Mello,et al.  Image analysis and rule-based reasoning for a traffic monitoring system , 1999, Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No.99TH8383).

[5]  David Zhang,et al.  Moving Vehicle Detection for Automatic Traffic Monitoring , 2007, IEEE Transactions on Vehicular Technology.

[6]  Marko Heikkilä,et al.  A texture-based method for modeling the background and detecting moving objects , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Xiaodong Cai,et al.  Robust Online Video Background Reconstruction Using Optical Flow and Pixel Intensity Distribution , 2008, 2008 IEEE International Conference on Communications.

[8]  Jian Zhang,et al.  Vehicle Classification at Nighttime Using Eigenspaces and Support Vector Machine , 2008, 2008 Congress on Image and Signal Processing.

[9]  Gabriele Monfardini.  Recursive Neural Networks for the Classification of Vehicles in Image Sequences , 2004, WIRN.

[10]  Michel Bilodeau,et al.  Road segmentation and obstacle detection by a fast watershed transformation , 1994, Proceedings of the Intelligent Vehicles '94 Symposium.

[11]  Mei Yu,et al.  Vision Based Vehicle Detection and Traffic Parameter Extraction , 2001 .

[12]  A. Bozzoli,et al.  A tunable algorithm to update a reference image , 2000, Signal Process. Image Commun..

[13]  Bo Yang,et al.  VISATRAM: a real-time vision system for automatic traffic monitoring , 2000, Image Vis. Comput..

[14]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[15]  Jie Zhou,et al.  Adaptive background estimation for real-time traffic monitoring , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[16]  Arnaldo J. Abrantes,et al.  Automatic Vehicle Detection and Classification , 2008, 2008 11th International IEEE Conference on Intelligent Transportation Systems.

[17]  N. Armanfard,et al.  A non-parametric pixel-based background modeling for dynamic scenes , 2009, 2009 International Conference on Advances in Computational Tools for Engineering Applications.

[18]  Cláudio Rosito Jung,et al.  A robust linear-parabolic model for lane following , 2004, Proceedings. 17th Brazilian Symposium on Computer Graphics and Image Processing.