Foreground Objects Segmentation for Moving Camera Scenarios Based on SCGMM

In this paper we present a new system for segmenting non-rigid objects in moving camera sequences for indoor and outdoor scenarios that achieves a correct object segmentation via global MAP-MRF framework formulation for the foreground and background classification task. Our proposal, suitable for video indexation applications, receives as an input an initial segmentation of the object to segment and it consists of two region-based parametric probabilistic models to model the spatial (x,y) and color (r,g,b) domains of the foreground and background classes. Both classes rival each other in modeling the regions that appear within a dynamic region of interest that includes the foreground object to segment and also, the background regions that surrounds the object. The results presented in the paper show the correctness of the object segmentation, reducing false positive and false negative detections originated by the new background regions that appear near the region of the object.

[1]  Huijun Di,et al.  Background modeling from a free-moving camera by Multi-Layer Homography Algorithm , 2008, 2008 15th IEEE International Conference on Image Processing.

[2]  José María Martínez Sanchez,et al.  A ground truth for motion-based video-object segmentation , 2008, 2008 15th IEEE International Conference on Image Processing.

[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]  G. Fasano,et al.  A multidimensional version of the Kolmogorov–Smirnov test , 1987 .

[5]  Mubarak Shah.  Tracking people in presence of occlusion , 2000 .

[6]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.

[7]  Olga Veksler,et al.  Fast Approximate Energy Minimization via Graph Cuts , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Ehud Rivlin,et al.  Bittracker—A Bitmap Tracker for Visual Tracking under Very General Conditions , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[10]  Michael F. Cohen,et al.  Monocular Video Foreground/Background Segmentation by Tracking Spatial-Color Gaussian Mixture Models , 2007, 2007 IEEE Workshop on Motion and Video Computing (WMVC'07).

[11]  Stephen M. Smith,et al.  ASSET-2: real-time motion segmentation and shape tracking , 1995, Proceedings of IEEE International Conference on Computer Vision.

[12]  Naokazu Yokoya,et al.  Real-Time Tracking of Multiple Moving Object Contours in a Moving Camera Image Sequence , 2000 .

[13]  Alex Pentland,et al.  Pfinder: real-time tracking of the human body , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[14]  Georgios Tziritas,et al.  A semi-automatic seeded region growing algorithm for video object localization and tracking , 2001, Signal Process. Image Commun..

[15]  Montse Pardàs,et al.  Bayesian foreground segmentation and tracking using pixel-wise background model and region based foreground model , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[16]  Yaser Sheikh,et al.  Bayesian modeling of dynamic scenes for object detection , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Harpreet S. Sawhney,et al.  Compact Representations of Videos Through Dominant and Multiple Motion Estimation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Yingzi Du,et al.  Iris Recognition: The Consequences of Image Compression , 2010, EURASIP J. Adv. Signal Process..

[19]  Rita Cucchiara,et al.  Real-time motion segmentation from moving cameras , 2004, Real Time Imaging.

[20]  Vittorio Murino,et al.  Background Subtraction for Automated Multisensor Surveillance: A Comprehensive Review , 2010, EURASIP J. Adv. Signal Process..

[21]  Montse Pardàs,et al.  Enhanced Bayesian foreground segmentation using Brightness and Color Distortion region-based model for shadow removal , 2010, 2010 IEEE International Conference on Image Processing.

[22]  S Kullback,et al.  LETTER TO THE EDITOR: THE KULLBACK-LEIBLER DISTANCE , 1987 .