Moving vehicles detection based on adaptive motion histogram

As one of the important topics in computer vision, moving vehicle segmentation has attracted considerable attention of researchers. However, robust detection is hampered by the interferential moving objects in dynamic scenes. In this paper, we address the problem of the moving vehicles segmentation in the dynamic scenes. Based on the distinct motion property of the dynamic background and that of the moving vehicles, we present an adaptive motion histogram for moving vehicles segmentation. The presented algorithm consists of two procedures: adaptive background update and motion histogram-based vehicles segmentation. In the adaptive background update procedure, we make use of the lighting change of the scene and present a novel method for background evolving. In the motion histogram-based vehicles segmentation procedure, an adaptive motion histogram is maintained and updated according to the motion information in the scenes, and the moving vehicles are then detected according to the motion histogram maintained. Experimental results of typical scenes demonstrate robustness of the proposed method. Quantitative evaluation and comparison with the existing methods show that the proposed method provides much improved results.

[1]  Richard Bowden,et al.  A real time adaptive visual surveillance system for tracking low-resolution colour targets in dynamically changing scenes , 2003, Image Vis. Comput..

[2]  Luigi di Stefano,et al.  A change-detection algorithm based on structure and colour , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[3]  Jake K. Aggarwal,et al.  Tracking and Segmentation of Highway Vehicles in Cluttered and Crowded Scenes , 2008, 2008 IEEE Workshop on Applications of Computer Vision.

[4]  N. Otsu A threshold selection method from gray level histograms , 1979 .

[5]  Osama Masoud,et al.  Detection and classification of vehicles , 2002, IEEE Trans. Intell. Transp. Syst..

[6]  M. Ali Akber Dewan,et al.  Moving Object Detection for Real Time Video Surveillance: An Edge Based Approach , 2007, IEICE Trans. Commun..

[7]  Berna Erol,et al.  A Bayesian framework for Gaussian mixture background modeling , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[8]  Qi Tian,et al.  Statistical modeling of complex backgrounds for foreground object detection , 2004, IEEE Transactions on Image Processing.

[9]  Robert T. Collins,et al.  Online Figure-ground Segmentation with Edge Pixel Classification , 2008, BMVC.

[10]  Nikos Paragios,et al.  Background modeling and subtraction of dynamic scenes , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[11]  Wen Gao,et al.  Modeling Background and Segmenting Moving Objects from Compressed Video , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Guohui Zhang,et al.  Video-Based Vehicle Detection and Classification System for Real-Time Traffic Data Collection Using Uncalibrated Video Cameras , 2007, Transportation Research Record: Journal of the Transportation Research Board.

[13]  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).

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

[15]  Tiziana D'Orazio,et al.  Moving object segmentation by background subtraction and temporal analysis , 2006, Image Vis. Comput..

[16]  Ming-Ting Sun,et al.  Fast variable-size block motion estimation using merging procedure with an adaptive threshold , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[17]  Jae-Young Choi,et al.  Multiple Vehicles Detection and Tracking based on Scale-Invariant Feature Transform , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[18]  Kyu-Won Lee,et al.  Moving object segmentation based on statistical motion model , 1999 .

[19]  Xiangzhong Fang,et al.  Spatiotemporal Gaussian mixture model to detect moving objects in dynamic scenes , 2007, J. Electronic Imaging.

[20]  Ying Liu,et al.  An HMM/MRF-based stochastic framework for robust vehicle tracking , 2004, IEEE Transactions on Intelligent Transportation Systems.

[21]  Jorge P. Batista,et al.  Robust segmentation for outdoor traffic surveillance , 2008, 2008 15th IEEE International Conference on Image Processing.

[22]  Kazuhiko Sumi,et al.  Background subtraction based on cooccurrence of image variations , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[23]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[24]  Zhiqiang Wei,et al.  Effective vehicle detection technique for traffic surveillance systems , 2006, J. Vis. Commun. Image Represent..

[25]  Y. Sun,et al.  Hierarchical GMM to handle sharp changes in moving object detection , 2004 .

[26]  Chengcui Zhang,et al.  Learning-based spatio-temporal vehicle tracking and indexing for transportation multimedia database systems , 2003, IEEE Trans. Intell. Transp. Syst..