I think the paper is great in dealing with different issues related to tracking. Tracking has traditionally done using some kind of adaptive filtering (ex. Kalman Filtering ). Again the idea remains the same. The parameters involved in tracking are not calcuated from scratch but are iteratively updated based on the previous information. All adaptive algorithms use a steps for tracking:

1) Predict: From previous data the new parameters are predicted.
2) Update: In this stage a search is performed in the neighborbood of the predicted parameters to search for the best match.

The algorithm presented in this paper essentially does the same. All the kinds of motion described can be modeled in Kalman Filter also. It would be nice if we can know how it compares with tracking using some kind of adaptive filters?

The inclusion of illumination effects and occlusion etc would definitely make this algorithm superior to conventional tracking but I was interested to know if it is indeed superior when there is no occlusion and illumination changes?