Extraction of Moving Objects Using Frame Differencing, Ghost and Shadow Removal

This paper proposes a technique for extracting moving object based on temporal differencing, ghost removal and shadow removal using NCC, while using a non-static Pan-Tilt-Zoom (PTZ) camera. To detect moving object in current image, the previous frame, ft-1, is compensated with respect to the current image. The subtraction of pixel intensity is done between the two aligned images. The used of temporal differencing resulting an appearance of 'ghost'. This paper proposes a technique to remove it by using the other previous image, ft-2. The resultant output is then cleaned up using morphological opening operator, before shadow removal is done. Each pre-defined foreground pixels are verified whether it is shadow pixel or foreground pixel. The results show the moving objects are extracted without shadow or other noises.

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

[2]  S. Murali,et al.  Segmentation of Motion Objects from Surveillance Video Sequences Using Temporal Differencing Combined with Multiple Correlation , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[3]  S. N. Yaakob,et al.  Moving object extraction in PTZ camera using the integration of background subtraction and local histogram processing , 2012, 2012 International Symposium on Computer Applications and Industrial Electronics (ISCAIE).

[4]  Atsushi Yamashita,et al.  Removal of adherent noises from images of dynamic scenes by using a pan-tilt camera , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[5]  Safvan Vahora A Robust Method for Moving Object Detection Using Modified Statistical Mean Method , 2012 .

[6]  Du-Ming Tsai,et al.  Fast normalized cross correlation for defect detection , 2003, Pattern Recognit. Lett..

[7]  Amar Aggoun,et al.  Edge Detection Using Adaptive Local Histogram Analysis , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[8]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[9]  A. Barto,et al.  Improved Temporal Difference Methods with Linear Function Approximation , 2004 .

[10]  Azriel Rosenfeld,et al.  Tracking Groups of People , 2000, Comput. Vis. Image Underst..

[11]  Wilhelm Burger,et al.  Digital Image Processing - An Algorithmic Introduction using Java , 2008, Texts in Computer Science.

[12]  Touradj Ebrahimi,et al.  Shadow identification and classification using invariant color models , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[13]  Mohsen Ebrahimi Moghaddam,et al.  An impulse noise fading technique based on local histogram processing , 2009, 2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[14]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[15]  Soraia Raupp Musse,et al.  Background Subtraction and Shadow Detection in Grayscale Video Sequences , 2005, XVIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI'05).

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

[17]  Luo Juan,et al.  A comparison of SIFT, PCA-SIFT and SURF , 2009 .

[18]  S. Govindarajulu,et al.  A Comparison of SIFT, PCA-SIFT and SURF , 2012 .