FPGA-based object-extraction based on multimodal Σ-Δ background estimation

In this paper, we propose a robust and accurate algorithm based on a multimodal Σ-Δ background estimation to extract the moving objects in image sequence of size 768 x 576 pixels taken from a static camera. Σ-Δ estimation is used to compute two orders of temporal statistics for each pixel of the sequence providing a pixel-level decision framework. A serious limitation of this approach lies in the adaptation capability to certain complex scenes. In this paper, we avoid this limitation by modeling each pixel as mixture of three distributions to deal with complex scenes. We show that the enhanced performance is achieved by using the proposed algorithm. This paper describes also an FPGA-based implementation of the proposed algorithm at a very high frame rate that reaches to 1198 frames per second in a single low cost FPGA chip, which is adequate for most real-time vision applications.

[1]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Larry S. Davis,et al.  A Perturbation Method for Evaluating Background Subtraction Algorithms , 2003 .

[3]  Nikos Paragios,et al.  Motion-based background subtraction using adaptive kernel density estimation , 2004, CVPR 2004.

[4]  Dah-Jye Lee,et al.  A Fast and Accurate Tensor-based Optical Flow Algorithm Implemented in FPGA , 2007, 2007 IEEE Workshop on Applications of Computer Vision (WACV '07).

[5]  P. Wayne Power,et al.  Understanding Background Mixture Models for Foreground Segmentation , 2002 .

[6]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Javier Díaz,et al.  FPGA-based real-time optical-flow system , 2006, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Antoine Manzanera,et al.  A New Hybrid differential filter for Motion Detection , 2004, ICCVG.

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

[10]  Unai Bidarte,et al.  Hardware implementation of optical flow constraint equation using FPGAs , 2005, Comput. Vis. Image Underst..

[11]  Antoine Manzanera,et al.  A Robust and Computationally Efficient Motion Detection Algorithm Based on Sigma-Delta Background Estimation , 2004, ICVGIP.

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

[13]  Alex Pentland,et al.  Pfinder: Real-Time Tracking of the Human Body , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Nigel J. B. McFarlane,et al.  Segmentation and tracking of piglets in images , 1995, Machine Vision and Applications.

[15]  Kentaro Toyama,et al.  Wallflower: principles and practice of background maintenance , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.