抄録
This paper presents an approach for real-time human activity recognition. Three different kinds of features (flow, shape, and a keypoint-based feature) are applied in activity recognition. We use random forests for feature integration and activity classification. A forest is created at each feature that performs as a weak classifier. The international classification of functioning, disability and health (ICF) proposed by WHO is applied in order to set the novel definition in activity recognition. Experiments on human activity recognition using the proposed framework show - 99.2% (Weizmann action dataset), 95.5% (KTH human actions dataset), and 54.6% (UCF50 dataset) recognition accuracy with a real-time processing speed. The feature integration and activity-class definition allow us to accomplish high-accuracy recognition match for the state-of-the-art in real-time.
| 本文言語 | English |
|---|---|
| ホスト出版物のタイトル | Seventh International Conference on Machine Vision, ICMV 2014 |
| 編集者 | Branislav Vuksanovic, Jianhong Zhou, Antanas Verikas, Petia Radeva |
| 出版社 | SPIE |
| ISBN（電子版） | 9781628415605 |
| DOI | |
| 出版ステータス | Published - 2015 |
| イベント | 7th International Conference on Machine Vision, ICMV 2014 - Milan, Italy 継続期間: 2014 11 19 → 2014 11 21 |
出版物シリーズ
| 名前 | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| 巻 | 9445 |
| ISSN（印刷版） | 0277-786X |
| ISSN（電子版） | 1996-756X |
Other
| Other | 7th International Conference on Machine Vision, ICMV 2014 |
|---|---|
| 国/地域 | Italy |
| City | Milan |
| Period | 14/11/19 → 14/11/21 |
ASJC Scopus subject areas
- 電子材料、光学材料、および磁性材料
- 凝縮系物理学
- コンピュータ サイエンスの応用
- 応用数学
- 電子工学および電気工学
UN SDG
この成果は、次の持続可能な開発目標に貢献しています
