An Incremental Approach of Clustering for Human Activity Discovery

• Ong Wee-Hong

  Graduate School of Engineering, The University of Tokyo

• Palafox Leon

  Department of Radiology, University of California

• Koseki Takafumi

  Graduate School of Engineering, The University of Tokyo

抄録

One of the challenges in human activity recognition is the ability for an intelligent system to discover the activity models by itself. In this paper, we propose an incremental approach to discover human activities from unlabeled data using K-means. The approach does not require prior specification of the number of clusters, or k-value, and has the ability to reject random movements or noise. Simple algorithm is used making the approach easy to implement without requiring any prior knowledge in the data. We evaluated the effectiveness of the approach and the results show more than 30% improvement in precision and 19% improvement in recall when compared to the results obtained using a non-incremental approach with cluster validity index. The achievement in human activity discovery will enable the wide adoption of human activity recognition technologies in the natural human living environment where labeled data are not available.

収録刊行物

• 電気学会論文誌Ｃ（電子・情報・システム部門誌）

  電気学会論文誌Ｃ（電子・情報・システム部門誌） 134 (11), 1724-1730, 2014

  一般社団法人 電気学会

キーワード

• human activity detection
• human activity discovery
• unsupervised learning
• clustering
• RGB-D sensor

詳細情報

• CRID: 1390282679585755776
• NII論文ID: 130004704432
• NII論文ID: 40020264692
• NII書誌ID: AN10065950
• DOI: 10.1541/ieejeiss.134.1724
• ISSN: 13488155
• ISSN: 03854221
• NDL書誌ID: 025913558
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I025913558
• Web Site: https://www.jstage.jst.go.jp/article/ieejeiss/134/11/134_1724/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可