A Novel Tracking Method Based on Improved FAST Corner Detection and Pyramid LK Optical Flow

Object tracking is a challenging problem with accuracy and large motion in computer vision. Though optical flow has achieved great performance, it would possibly fail to track fast moving object, and accuracy and big movement both suffer from the size of integration window. Pyramid LK (Lucas-Kanade) method can solve large motion in a small integration window. However, the basic pyramid LK is affected by accuracy. So, a combination of improved FAST corner detection and pyramid LK optical flow is applied. In this method, sub-pixel computation can increase the accuracy. The large movement can be satisfied by scaling the source image. The strongest gray changing corner point can be rapidly extracted by the improved FAST algorithm. This approach can better estimate the optical flow with strong corner to track moving object. In the experiment, a video of actual movement of ball is used to test algorithm validity. Average errors in the x direction and y direction are 0.9386 pixel and 0.6792 pixel separately. The detection results demonstrate that this algorithm can track moving target of large motion accurately.