Robust Contour Line Extraction Using Context

In this paper, a robust approach to contour line extraction in cluttered images using context is presented. Object contours are often used as key features in model-based 2D and 3D tracking systems. In many applications, object contours can be approximated using line segments, e.g. in human limb tracking. In cluttered images, undesired edges other than the true contour lines often present severe disturbance for reliable object tracking. In our approach, we reduce the effect of unwanted edges by exploring context information, including edge orientation and previous contour line position and orientation. Experimental results have shown that by using context, the number of false contour lines can be reduced significantly, which will greatly improve the tracking performance