Object Tracking using Correlation, Kalman Filter and Fast Means Shift Algorithms

Object detection in videos involves verifying the presence of an object in image sequences and possibly locating it precisely for recognition. Object tracking is to monitor an object's spatial and temporal changes during a video sequence, including its presence, position, size, shape, etc. This is done by solving the temporal correspondence problem, the problem of matching the target region in successive frames of a sequence of images taken at closely-spaced time intervals. These two processes are closely related because tracking usually starts with detecting objects, while detecting an object repeatedly in subsequent image sequence is often necessary to help and verify tracking. In this paper, a novel approach is being presented for object tracking. It includes combination of 2D normalized correlation, Kalman filter and fast mean shift algorithm