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The use of computer vision techniques to augment home based sensorised environments
Z. Uhríková, C.D. Nugent, V. Hlaváč
Jazyk angličtina Země Spojené státy americké
- MeSH
- algoritmy MeSH
- audiovizuální záznam MeSH
- činnosti denního života MeSH
- financování organizované MeSH
- lidé MeSH
- pohyb MeSH
- pomůcky pro sebeobsluhu MeSH
- postižení zákonodárství a právo MeSH
- reprodukovatelnost výsledků MeSH
- studie pohybu a času MeSH
- vnímání pohybu fyziologie MeSH
- životní prostředí MeSH
- zrak MeSH
- Check Tag
- lidé MeSH
Technology within the home environment is becoming widely accepted as a means to facilitate independent living. Nevertheless, practical issues of detecting different tasks between multiple persons within the same environment along with managing instances of uncertainty associated with recorded sensor data are two key challenges yet to be fully solved. This work presents details of how computer vision techniques can be used as both alternative and complementary means in the assessment of behaviour in home based sensorised environments. Within our work we assessed the ability of vision processing techniques in conjunction with sensor based data to deal with instances of multiple occupancy. Our Results indicate that the inclusion of the video data improved the overall process of task identification by detecting and recognizing multiple people in the environment using color based tracking algorithm.
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- $a Technology within the home environment is becoming widely accepted as a means to facilitate independent living. Nevertheless, practical issues of detecting different tasks between multiple persons within the same environment along with managing instances of uncertainty associated with recorded sensor data are two key challenges yet to be fully solved. This work presents details of how computer vision techniques can be used as both alternative and complementary means in the assessment of behaviour in home based sensorised environments. Within our work we assessed the ability of vision processing techniques in conjunction with sensor based data to deal with instances of multiple occupancy. Our Results indicate that the inclusion of the video data improved the overall process of task identification by detecting and recognizing multiple people in the environment using color based tracking algorithm.
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