Towards fish-eye camera based in-home activity assessment.
Indoors localization, activity classification, and behavioral modeling are increasingly important for surveillance applications including independent living and remote health monitoring. In this paper, we study the suitability of fish-eye cameras (high-resolution CCD sensors with very-wide-angle lenses) for the purpose of monitoring people in indoors environments. The results indicate that these sensors are very useful for automatic activity monitoring and people tracking. We identify practical and mathematical problems related to information extraction from these video sequences and identify future directions to solve these issues.
Bas
Erhan
E
Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA. base@csee.ogi.edu
Erdogmus
Deniz
D
Ozertem
Umut
U
Pavel
Misha
M
D000465:Algorithms; D001185:Artificial Intelligence; D004777:Environment; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D010780:Photogrammetry / Q000379:methods*; D015203:Reproducibility of Results; D014741:Video Recording / Q000379:methods*