Minimizing Human Effort in Interactive Tracking by Incremental Learning of Model Parameters

We address the problem of minimizing human effort in interactive tracking by learning sequence-specific model parameters. Determining the optimal model parameters for each sequence is a critical problem in tracking. We demonstrate that by using the optimal model parameters for each sequence we can achieve high precision tracking results with significantly less effort. We leverage the sequential nature of interactive tracking to formulate an efficient method for learning model parameters through a maximum margin framework. By using our method we are able to save ~60 -- 90% of human effort to achieve high precision on two datasets: the VIRAT dataset and an Infant-Mother Interaction dataset.