@inproceedings{4d7cdbf995b9400894623c7a25e3a8e9,
title = "TaiChi: A fine-grained action recognition dataset",
abstract = "In this paper, we introduce TaiChi which is a fine-grained action dataset. It consists of unconstrained user-uploaded web videos containing camera motion and partial occlusions which pose new challenges to fine-grained action recognition compared to the existing datasets. In this dataset, 2, 772 samples of 58 fine-grained action classes are manually annotated. Additionally, we provide the baseline action recognition results using the state-of-the-art Improved Dense Trajectory feature and Fisher Vector representation with an MAP (Mean Average Precision) of 51.39\%.",
keywords = "Benchmark dataset, Fine-grained action recognition dataset, Tai chi",
author = "Shan Sun and Feng Wang and Qi Liang and Liang He",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 17th ACM International Conference on Multimedia Retrieval, ICMR 2017 ; Conference date: 06-06-2017 Through 09-06-2017",
year = "2017",
month = jun,
day = "6",
doi = "10.1145/3078971.3079039",
language = "英语",
series = "ICMR 2017 - Proceedings of the 2017 ACM International Conference on Multimedia Retrieval",
publisher = "Association for Computing Machinery, Inc",
pages = "429--433",
booktitle = "ICMR 2017 - Proceedings of the 2017 ACM International Conference on Multimedia Retrieval",
}