Improving the Efficiency of Background Subtraction using Superpixel Extraction and Midpoint for Centroid

This paper deals with an efficient background subtraction of image/frames of video by improving the execution speed, accuracy and reduce the usage of memory. Three important techniques are applied to improve the efficiency: superpixel extraction, canny edge detection and fuzzy c means. On applying the above three methods sequentially, the background of image/video can be segmented from foreground object accurately. The first method reduces the processing data more than 75%. Canny edge detection is an optimized method to detect edges. Fuzzy c means works well and good to segment the overlapped objects in an image/video. General Terms Canny edge detection, Feature extraction, Fuzzy c means, Gradient, Segmentation, Smoothing.

[1]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  David Suter,et al.  A re-evaluation of mixture of Gaussian background modeling [video signal processing applications] , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[3]  Mubarak Shah,et al.  A hierarchical approach to robust background subtraction using color and gradient information , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[4]  Jitendra Malik,et al.  Recovering human body configurations: combining segmentation and recognition , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[5]  Larry S. Davis,et al.  Fast illumination-invariant background subtraction using two views: error analysis, sensor placement and applications , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Yeung Sam Hung,et al.  A Hierarchical Approach for Fast and Robust Ellipse Extraction , 2007, 2007 IEEE International Conference on Image Processing.

[7]  J. Canny.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Thomas B. Moeslund.  Image and Video Processing , 2008 .

[9]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[10]  Sergei Azernikov.  Sweeping solids on manifolds , 2008, SPM '08.

[11]  Dale Schuurmans,et al.  Real-Time Discriminative Background Subtraction , 2011, IEEE Transactions on Image Processing.

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

[13]  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).

[14]  Jitendra Malik,et al.  Learning a classification model for segmentation , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

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

[16]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[17]  Supun Samarasekera,et al.  Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation , 1996, CVGIP Graph. Model. Image Process..

[18]  De Xu,et al.  Fusing Color and Gradient Features for Background Model , 2006, 2006 8th international Conference on Signal Processing.

[19]  Greg Mori,et al.  Guiding model search using segmentation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[20]  Z. Zivkovic.  Improved adaptive Gaussian mixture model for background subtraction , 2004, ICPR 2004.

[21]  Aaron F. Bobick,et al.  Fast Lighting Independent Background Subtraction , 2004, International Journal of Computer Vision.

[22]  Chandrika Kamath,et al.  Robust Background Subtraction with Foreground Validation for Urban Traffic Video , 2005, EURASIP J. Adv. Signal Process..