Abstract:
In this paper we propose a unified action recognition framework fusing local descriptors and holistic features. The motivation is that the local descriptors and holistic ...Show MoreMetadata
Abstract:
In this paper we propose a unified action recognition framework fusing local descriptors and holistic features. The motivation is that the local descriptors and holistic features emphasize different aspects of actions and are suitable for the different types of action databases. The proposed unified framework is based on frame differencing, bag-of-words and feature fusion. We extract two kinds of local descriptors, i.e. 2D and 3D SIFT feature descriptors, both based on 2D SIFT interest points. We apply Zernike moments to extract two kinds of holistic features, one is based on single frames and the other is based on motion energy image. We perform action recognition experiments on the KTH and Weizmann databases, using Support Vector Machines. We apply the leave-one-out and pseudo leave-N-out setups, and compare our proposed approach with state-of-the-art results. Experiments show that our proposed approach is effective. Compared with other approaches our approach is more robust, more versatile, easier to compute and simpler to understand.
Published in: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Date of Conference: 20-25 June 2009
Date Added to IEEE Xplore: 18 August 2009
ISBN Information: