Moving Objects Tracking Using Statistical Models

Object detection plays an important role in successfulness of a wide range of applications that involve images as input data. In this paper we have presented a new approach for background modeling by nonconsecutive frames differencing. Direction and velocity of moving objects have been extracted in order to get an appropriate sequence of frames to perform frame subtraction. Stationary parts of background are extracted from differenced frames and joined as patches to complete the background model. There is also a special stage to handle changing regions of dynamic scenes. During the detection phase, the modeled background is updated for every new frame. Since it's not necessary to estimate each pixel gray value like the most common statistical methods, modeling process is not timeconsuming. Different experiments show successful results even for challenging phenomena like environmental changes.

[1]  P. KaewTrakulPong,et al.  An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .

[2]  L. Davis,et al.  Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002, Proc. IEEE.

[3]  K. P. Karmann,et al.  Moving object recognition using an adaptive background memory , 1990 .

[4]  H. Grabner,et al.  Autonomous Learning of a Robust Background Model for Change Detection ∗ , 2006 .

[5]  Jitendra Malik,et al.  Towards robust automatic traffic scene analysis in real-time , 1994, Proceedings of 12th International Conference on Pattern Recognition.

[6]  Larry S. Davis,et al.  Non-parametric Model for Background Subtraction , 2000, ECCV.

[7]  D. Koller,et al.  Towards robust automatic traffic scene analysis in real-time , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[8]  Joachim M. Buhmann,et al.  Topology Free Hidden Markov Models: Application to Background Modeling , 2001, ICCV.

[9]  Fatih Murat Porikli,et al.  A Bayesian Approach to Background Modeling , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[10]  Andrew Blake,et al.  A Probabilistic Background Model for Tracking , 2000, ECCV.

[11]  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).