Affine-Invariant Feature Extraction for Activity Recognition. (15th July 2013)

• Record Type: Journal Article
• Authors: Sadek, Samy; Al-Hamadi, Ayoub; Krell, Gerald; Michaelis, Bernd
• Other Names: Gasteratos A. Academic Editor.; Mukherjee D. P. Academic Editor.; Torsello A. Academic Editor.
• Is Part Of: ISRN machine vision. Volume 2013(2013)
• Publication Date: 2013-07-15
• Subjects: Computer vision -- Periodicals; Computer vision; Periodicals; Electronic journals; 006.37
• DOI: 10.1155/2013/215195
• Languages: English
• ISSNs: 2090-7796

Abstract

Abstract : We propose an innovative approach for human activity recognition based on affine-invariant shape representation and SVM-based feature classification. In this approach, a compact computationally efficient affine-invariant representation of action shapes is developed by using affine moment invariants. Dynamic affine invariants are derived from the 3D spatiotemporal action volume and the average image created from the 3D volume and classified by an SVM classifier. On two standard benchmark action datasets (KTH and Weizmann datasets), the approach yields promising results that compare favorably with those previously reported in the literature, while maintaining real-time performance.