Local Geometrically Enriched Mixtures for Stable and Robust Human Tracking in Detecting Falls
Detecting a fall through visual cues is emerging as a hot research agenda for improving the independence of the elderly. However, the traditional motion-based algorithms are very sensitive to noise, reducing fall detection accuracy. Another approach is to efficiently localize and then track the fore...
Main Authors: | Michalis Kokkinos, Nikolaos D. Doulamis, Anastasios D. Doulamis |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2013-01-01
|
Series: | International Journal of Advanced Robotic Systems |
Online Access: | https://doi.org/10.5772/54049 |
Similar Items
-
Fall Detection Using Multi-Property Spatiotemporal Autoencoders in Maritime Environments
by: Iason Katsamenis, et al.
Published: (2022-03-01) -
EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation
by: Maria Kaselimi, et al.
Published: (2021-01-01) -
On the Exploration of Automatic Building Extraction from RGB Satellite Images Using Deep Learning Architectures Based on U-Net
by: Anastasios Temenos, et al.
Published: (2022-01-01) -
Stacked Autoencoders Driven by Semi-Supervised Learning for Building Extraction from near Infrared Remote Sensing Imagery
by: Eftychios Protopapadakis, et al.
Published: (2021-01-01) -
ELECTRIcity: An Efficient Transformer for Non-Intrusive Load Monitoring
by: Stavros Sykiotis, et al.
Published: (2022-04-01)