@inproceedings{c5f26779c91c4a2d844de015dc4740fa,
title = "Minimizing human effort in interactive tracking by incremental learning of model parameters",
abstract = "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.",
author = "Arridhana Ciptadi and Rehg, {James M.}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 15th IEEE International Conference on Computer Vision, ICCV 2015 ; Conference date: 11-12-2015 Through 18-12-2015",
year = "2015",
month = feb,
day = "17",
doi = "10.1109/ICCV.2015.498",
language = "English (US)",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "4382--4390",
booktitle = "2015 International Conference on Computer Vision, ICCV 2015",
address = "United States",
}