Eigenvalue based corner detection is known to be effective in detecting corners of objects in noise. In this paper, a comer detection technique based on including the orientation and the angle of the comer in addition to its eigenvalue is introduced. It is shown that both orientation and comer information improve the detectability of corners. Moreover, comers 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 scheme. Real and synthetic images are used to evaluate the detection performance as well as their effect on tracking.