@inproceedings{18af049c86774f16a562a0afb9202651,
title = "Improving human action recognition using score distribution and ranking",
abstract = "We propose two complementary techniques to improve the performance of action recognition systems. The first technique addresses the temporal interval ambiguity of actions by learning a classifier score distribution over video subsequences. A classifier based on this score distribution is shown to be more effective than using the maximum or average scores. The second technique learns a classifier for the relative values of action scores, capturing the correlation and exclusion between action classes. Both techniques are simple and have efficient implementations using a Least-Squares SVM. We demonstrate that taken together the techniques exceed the state-of-the-art performance by a wide margin on challenging benchmarks for human actions.",
author = "Minh Hoai and Andrew Zisserman",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 12th Asian Conference on Computer Vision, ACCV 2014 ; Conference date: 01-11-2014 Through 05-11-2014",
year = "2015",
doi = "10.1007/978-3-319-16814-2\_1",
language = "English",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "3--20",
editor = "Daniel Cremers and Hideo Saito and Ian Reid and Ming-Hsuan Yang",
booktitle = "Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers",
}