@inproceedings{cf930a1fe64d4f73b60f15e27af734ce,
title = "Movement pattern histogram for action recognition and retrieval",
abstract = "We present a novel action representation based on encoding the global temporal movement of an action. We represent an action as a set of movement pattern histograms that encode the global temporal dynamics of an action. Our key observation is that temporal dynamics of an action are robust to variations in appearance and viewpoint changes, making it useful for action recognition and retrieval. We pose the problem of computing similarity between action representations as a maximum matching problem in a bipartite graph. We demonstrate the effectiveness of our method for cross-view action recognition on the IXMAS dataset. We also show how our representation complements existing bag-of-features representations on the UCF50 dataset. Finally we show the power of our representation for action retrieval on a new real-world dataset containing repetitive motor movements emitted by children with autism in an unconstrained classroom setting.",
author = "Arridhana Ciptadi and Goodwin, {Matthew S.} and Rehg, {James M.}",
year = "2014",
doi = "10.1007/978-3-319-10605-2_45",
language = "English (US)",
isbn = "9783319106045",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
number = "PART 2",
pages = "695--710",
booktitle = "Computer Vision, ECCV 2014 - 13th European Conference, Proceedings",
address = "Germany",
edition = "PART 2",
note = "13th European Conference on Computer Vision, ECCV 2014 ; Conference date: 06-09-2014 Through 12-09-2014",
}