Track validation using gradient-based normalised cross-correlation

We develop a gradient-based normalised cross-correlation tracker that is as robust as brute-force template matching while being significantly more computationally efficient. The technique serves as the basis of our track validation algorithm: by tracking an object forwards in time, reinitialising at the end of the sequence and then tracking backwards in time, we can determine whether or not the object has been followed correctly ‐ the forwards and backwards trajectories will be very different for tracking failures. If such a failure occurs, we iteratively attempt to validate shorter portions of the video sequence until validation is achieved. The algorithm provides a means of determining whether or not an object was tracked successfully without the need for ground truth data.