Correlation-based self-correcting tracking

We present a framework for improving probabilistic tracking of an extended object with a set of model points. The framework combines the tracker with an on-line performance measure and a correction technique. We correlate model point trajectories to improve on-line the accuracy of a failed or an uncertain tracker. A model point tracker gets assistance from neighboring trackers whenever a degradation in its performance is detected using the on-line performance measure. The correction of the model point state is based on correlation information from the state of other trackers. Partial Least Square (PLS) regression is used to model the correlation of point tracker states from short windowed trajectories adaptively. Experimental results on data obtained from optical motion capture systems show the improvement in tracking performance of the proposed framework compared to the baseline tracker and other state-of-the-art trackers.