1. Implementation on video object segmentation algorithm
Kuo, Yi-Ting and Wu, Chia-Peng
May

2. Outlines Introduction Algorithm
Architecture of hardware implementation
Systolic array for texture feature extraction

3. Introduction
Our project is focused on extracting moving objects from video.
The algorithm of moving object segmentation can be applied to MPEG-4 standard which enable content-based functionality.
Also can be used in traffic surveillance system.

4. Algorithm Change detection Previous frame In-1 Current Frame In
Find moving
object edge
Smooth edge
Moving object －

5. Mean and Variance Features
The two features (mean and variance),ft1(m,n) and ,ft2(m,n) are
textural appearance of the area surrounding a pixel (m,n) in a
small window centered on this pixel, Nw is the number of pixel
of Ws * Ws of window W .

6. Systolic array for texture feature extraction

7. Systolic array for extracting the two texture features ft1, ft2
Systolic array for extracting the two texture features using 5x5 window
The luminance component of a reference frame fy(m,n) are scanned
Into 1+4Nc size FIFO.
Nc = number of columns of reference frame
Block A: accumulates luminance components.
Block M: generate a mean value by dividing the accumulated result by Nw
Block V: calculate localvariance texture feature.

8. DEMO

9. References
[1] Changick Kim and Jenq-Neng Hwang, “Fast and automatic video object segmentation and tracking for content-based application,” IEEE Trans. Circuits and Systems for Video Technology, vol. 12, No. 2, Feb. 2002, pp
[2] J. F. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-6, pp , Nov
[3] Jinsang Kim and Tom Chen, “Real-time video objects segmentation using a highly pipelined microarchitecture,” Proceedings of the IASTED International Conference, Visualization, Imaging, and Image Processing, Sep. 3-5, 2001, Marbella, Spain, pp
[4] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing.