A compact 3D descriptor in ROI for human action recognition

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

5 被引用数 (Scopus)


In this paper, a new action recognition system is proposed, which employs 3D FAST corner detection in ROI, compact 3D descriptor to represent action information, and SOM to learn and recognize actions. Through detecting 3D FAST corners in ROI, action information of shape and motion can be obtained, and noise corners can be deleted at the same time. Furthermore, based on 3D HOG, we produce a simpler descriptor which is proposed by shortening the support region of interest points, combining symmetric bins after orientation quantization using icosahedron, and keeping the top value bin of quantized histogram. Compared with the descriptor before adjustment, our descriptor uses only 80 bins other than 960 bins to describe one interest point, which saves much computation time and memory. Our frame matching experiment on descriptor also certifies that our descriptor outperforms the previous one. Our descriptor is applied to recognize actions on KTH and Hollywood databases, and the results show that it performs well.

ホスト出版物のタイトルTENCON 2010 - 2010 IEEE Region 10 Conference
出版ステータス出版済み - 12 1 2010
イベント2010 IEEE Region 10 Conference, TENCON 2010 - Fukuoka, 日本
継続期間: 11 21 201011 24 2010


名前IEEE Region 10 Annual International Conference, Proceedings/TENCON


その他2010 IEEE Region 10 Conference, TENCON 2010

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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