@inproceedings{d6f20b7aa05940029809bfbbff85632b,
title = "Human action recognition based on sub-data learning",
abstract = "Human action recognizing nowadays plays a key role in varieties of computer vision applications while at the same time it{\textquoteright}s quite challenging for the requirement of accuracy and robustness. Most current computer vision methods focus on algorithms designing classifiers with handcrafted features which are complex and inflexible. To automatically extract both spatial and temporal features, in this paper we propose a method of human action recognition based on sub-data learning which combines the proposed 3D convolutional neural network (3DCNN) with the One-versus-One (OvO) algorithm. We also employ effective data augmentation to reduce overfitting. We evaluate our method on the KTH and UCF Sports dataset and achieve promising results.",
keywords = "3DCNN, Action recognition, Sub-data learning",
author = "Yang Chen and Tian Wang and Jiakun Li and Xiaowei Lv and Hichem Snoussi",
note = "Publisher Copyright: {\textcopyright} 2017, Springer Nature Singapore Pte Ltd.; 2nd Chinese Conference on Computer Vision, CCCV 2017 ; Conference date: 11-10-2017 Through 14-10-2017",
year = "2017",
doi = "10.1007/978-981-10-7305-2\_52",
language = "英语",
isbn = "9789811073045",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "617--626",
editor = "Jinfeng Yang and Qingshan Liu and Liang Wang and Xiang Bai and Qinghua Hu and Ming-Ming Cheng and Deyu Meng",
booktitle = "Computer Vision - 2nd CCF Chinese Conference, CCCV 2017, Proceedings",
address = "德国",
}