@inproceedings{9d3282cee9a846498278a2b8527cf7e0,
title = "A novel group-sparsity-optimization-based feature selection model for complex interaction recognition",
abstract = "Interaction recognition is an important part of action recognition and has various applications such as surveillance systems, human computer interface, and machine intelligence. In this paper, we propose a novel group-sparsity-optimization-based feature selection model for complex interaction recognition. Firstly multiple local and global features are concatenated into a feature pool, and then based on the group sparsity optimization, different feature types are automatically selected to fit specific interaction categorization. We test our method on the benchmark dataset: the UT-interaction dataset. Experimental results substantiate the effectiveness of the proposed method on complex interaction recognition tasks as compared with current state-of-the-art methods.",
author = "Luyu Yang and Chenqiang Gao and Deyu Meng and Lu Jiang",
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\_33",
language = "英语",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "508--521",
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",
}