Capturing 2½D Depth and Texture of Time-Varying Scenes Using Structured Infrared Light
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Human Activity Recognition in Thermal Infrared Imagery
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Depth silhouettes for gesture recognition
Pattern Recognition Letters
A review of smart homes-Present state and future challenges
Computer Methods and Programs in Biomedicine
A new shape descriptor defined on the Radon transform
Computer Vision and Image Understanding
Gender recognition from gait using radon transform and relevant component analysis
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Independent shape component-based human activity recognition via Hidden Markov Model
Applied Intelligence
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We present a human activity recognition (HAR) system for smart homes utilizing depth silhouettes and R transformation. Previously, R transformation has been applied only on binary silhouettes which provide only the shape information of human activities. In this work, we utilize R transformation on depth silhouettes such that the depth information of human body parts can be used in HAR in addition to the shape information. In R transformation, 2D directional projection maps are computed through Radon transform, and then 1D feature profiles, that are translation and scaling invariant, are computed through R transform. Then, we apply Principle Component Analysis and Linear Discriminant Analysis to extract prominent activity features. Finally, Hidden Markov Models are used to train and recognize daily home activities. Our results show the mean recognition rate of 96.55% over ten typical home activities whereas the same system utilizing binary silhouettes achieves only 85.75%. Our system should be useful as a smart HAR system for smart homes.