Modified GMM background modeling and optical flow for detection of moving objects

Segmentation of moving objects in image sequences is a fundamental step in many computer vision applications such as mineral processing industry and automated visual surveillance. In this paper, we introduce a novel approach to detect moving objects in a noisy background. Our approach combines a modified adaptive Gaussian mixture model (GMM) for background subtraction and optical flow methods supported by temporal differencing in order to achieve robust and accurate extraction of the shapes of moving objects. The algorithm works well for image sequences having many moving objects with different sizes as demonstrated by experimental results on real image sequences.

[1]  Ioannis Pitas,et al.  Optical flow estimation and moving object segmentation based on median radial basis function network , 1998, IEEE Trans. Image Process..

[2]  Koichi Sasakawa,et al.  Detection of small moving object by optical flow , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.

[3]  P. Anandan,et al.  Hierarchical Model-Based Motion Estimation , 1992, ECCV.

[4]  Badrinath Roysam,et al.  Image change detection algorithms: a systematic survey , 2005, IEEE Transactions on Image Processing.

[5]  Andrew Lippman,et al.  Spatio-temporal segmentation based on motion and static segmentation , 1995, Proceedings., International Conference on Image Processing.

[6]  Xinhua Zhuang,et al.  Optic Flow Field Segmentation and Motion Estimation Using a Robust Genetic Partitioning Algorithm , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Alan M. McIvor,et al.  Background Subtraction Techniques , 2000 .

[8]  Takeo Kanade,et al.  A System for Video Surveillance and Monitoring , 2000 .

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

[10]  Chandrika Kamath,et al.  Robust techniques for background subtraction in urban traffic video , 2004, IS&T/SPIE Electronic Imaging.

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

[12]  Liang-Gee Chen,et al.  Efficient moving object segmentation algorithm using background registration technique , 2002, IEEE Trans. Circuits Syst. Video Technol..

[13]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[14]  Tieniu Tan,et al.  Recent developments in human motion analysis , 2003, Pattern Recognit..

[15]  Dongxiang Zhou,et al.  2D Shape Measurement of Multiple Moving Objects by GMM Background Modeling and Optical Flow , 2005, ICIAR.

[16]  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).

[17]  Amir Averbuch,et al.  Automatic Adaptive Segmentation of Moving Objects Based on Spatio-Temporal Information , 2003, DICTA.

[18]  Shmuel Peleg,et al.  A Three-Frame Algorithm for Estimating Two-Component Image Motion , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Shunichiro Oe,et al.  A new gradient-based optical flow method and its application to motion segmentation , 2000, 2000 26th Annual Conference of the IEEE Industrial Electronics Society. IECON 2000. 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation. 21st Century Technologies.

[20]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[21]  Stuart J. Russell,et al.  Image Segmentation in Video Sequences: A Probabilistic Approach , 1997, UAI.

[22]  Takeo Kanade,et al.  Advances in Cooperative Multi-Sensor Video Surveillance , 1999 .