An Improved Mixture-of-Gaussians Background Model with Frame Difference and Blob Tracking in Video Stream

Modeling background and segmenting moving objects are significant techniques for computer vision applications. Mixture-of-Gaussians (MoG) background model is commonly used in foreground extraction in video steam. However considering the case that the objects enter the scenery and stay for a while, the foreground extraction would fail as the objects stay still and gradually merge into the background. In this paper, we adopt a blob tracking method to cope with this situation. To construct the MoG model more quickly, we add frame difference method to the foreground extracted from MoG for very crowded situations. What is more, a new shadow removal method based on RGB color space is proposed.

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

[2]  Carlos Cuevas,et al.  Moving object detection for real-time high-quality lightweight applications on smart cameras , 2011, 2011 IEEE International Conference on Consumer Electronics (ICCE).

[3]  Fernando Jaureguizar,et al.  High-quality real-time moving object detection by non-parametric segmentation , 2010 .

[4]  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).

[5]  Max Lu,et al.  Robust and efficient foreground analysis for real-time video surveillance , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Narciso García,et al.  Moving object detection for real-time augmented reality applications in a GPGPU , 2012, IEEE Transactions on Consumer Electronics.

[7]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  R. Cucchiara,et al.  Statistic and knowledge-based moving object detection in traffic scenes , 2000, ITSC2000. 2000 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.00TH8493).

[9]  Sridha Sridharan,et al.  Crowd Counting Using Multiple Local Features , 2009, 2009 Digital Image Computing: Techniques and Applications.

[10]  Zhang Zong-bin.  A moving objects segmentation algorithm based on improved GMM , 2010 .

[11]  Narciso García,et al.  Efficient Moving Object Detection for Lightweight Applications on Smart Cameras , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Rita Cucchiara,et al.  Improving shadow suppression in moving object detection with HSV color information , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[13]  Tian Jin-wen.  Video Segmentation Algorithm with Gaussian Mixture Model and Shadow Removal , 2008 .