HARDWARE DESIGN OF A HOUGH ESTIMATION SY STEM1 TRANSFORM BASED 2-D MOTION

A novel feature-domain 2D motion estimation system based on the straight-line Hough transform (SLHT) is presented. This system implements the motion estimation technique proposed in (5). It operates on 256 x 256-pixel binary images and consists of two main blocks. The first block does preprocessing work including smoothing the boundary, tracing and integrating the contours, and detecting dominant points. The second block computes the Hough transform on contour segments as well as the rotation and translation parameters. Each of the modules has been implemented (gate level) and simulated using Mentor Graphics tools. Finally, the experimental results have been presented and com- pared with the results of the software implementation. The motion estimation unit is a key component in systems for object tracking, robotic vision. dynamic scene understanding, and autonomous nav- igation. Most of the VLSI implementations of 2-D motion estimation sys- tems are based on the block matching algorithm (BMA), since BMA has been adopted in the MPEG standards. But, BMAs are pixel-domain based approaches and require a large amount of computations. Feature-domain based approaches, on the other hand, represent an object by a small set of features (such as shape features, moments, Fourier transforms), resulting in a reduction in the number of computations. In this paper, we present a novel feature-domain 2D motion estimation system based on the straight-line Hough transform. The system is mainly used for tracking objecis. Its design is based on the motion estimation tech- nique of (5) that represlents objects uniquely by straight-line approximations of the boundary {(e, p)}, and estimates the motion parameters from shifts in the B - p space. The system has been implemented using Mentor Graph- ics tools. It operates an 256 x 256-pixel binary images and consists of two