Eigenvalue based corner detection is known to be effective in detecting corners of objects in noise. In this paper, a corner detection technique based on including the orientation and the angle of the corner in addition to its eigenvalue is introduced. It is shown that both orientation and corner information improve the detectability of corners. Moreover, corners that have been selected via the new technique are more likely to be detected in subsequent frames and therefore improve the performance of an object tracker. This modification only adds a minor computational load to our tracking schemé. Real and synthetic images are used to evaluate the detection performance as well as their effect on tracking.
Tracking moving objects in video can be carried out by correlating a template containing object pixels of the current frame. This approach may produce erronous results under noise. We determine a set of significant pixels on the object by analyzing the wavelet transform of the template and correlate only these pixels with the current frame to determine the position of the object. These significant pixels are easily trackable features of the image and incrase the performance of the tracker.