TY - GEN
T1 - Semantic segmentation in egocentric video frames with deep learning for recognition of activities of daily living
AU - Zamorano Raya, José A.
AU - García Vázquez, Mireya S.
AU - Jaimes Méndez, Juan C.
AU - Montoya Obeso, Abraham
AU - Compean Aguirre, Jorge L.
AU - Ramírez Acosta, Alejandro A.
N1 - Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
PY - 2019
Y1 - 2019
N2 - The analysis of videos for the recognition of Instrumental Activities of Daily Living (IADL) through the detection of objects and the context analysis, applied for the evaluation of patient's capacity with Alzheimer's disease and age related dementia, has recently gained a lot of interest. The incorporation of human perception in the recognition tasks, search, detection and visual content understanding has become one of the main tools for the development of systems and technologies that support the performance of people in their daily life activities. In this paper we propose a model of automatic segmentation of the saliency region where the objects of interest are found in egocentric video using fully convolutional networks (FCN). The segmentation is performed with the information regarding to human perception, obtaining a better segmentation at pixel level. This segmentation involves objects of interest and the salient region in egocentric videos, providing precise information to detection systems and automatic indexing of objects in video, where these systems have improved their performance in the recognition of IADL. To measure models segmentation performance of the salient region, we benchmark two databases; first, Georgia-Tech-Egocentric-Activity database and second, our own database.
AB - The analysis of videos for the recognition of Instrumental Activities of Daily Living (IADL) through the detection of objects and the context analysis, applied for the evaluation of patient's capacity with Alzheimer's disease and age related dementia, has recently gained a lot of interest. The incorporation of human perception in the recognition tasks, search, detection and visual content understanding has become one of the main tools for the development of systems and technologies that support the performance of people in their daily life activities. In this paper we propose a model of automatic segmentation of the saliency region where the objects of interest are found in egocentric video using fully convolutional networks (FCN). The segmentation is performed with the information regarding to human perception, obtaining a better segmentation at pixel level. This segmentation involves objects of interest and the salient region in egocentric videos, providing precise information to detection systems and automatic indexing of objects in video, where these systems have improved their performance in the recognition of IADL. To measure models segmentation performance of the salient region, we benchmark two databases; first, Georgia-Tech-Egocentric-Activity database and second, our own database.
KW - Deep CNN
KW - Dementia diseases
KW - Egocentric video
KW - FCN
KW - Instrumental activities of daily living
KW - Object recognition
KW - Saliency
KW - Semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85075751605&partnerID=8YFLogxK
U2 - 10.1117/12.2529834
DO - 10.1117/12.2529834
M3 - Contribución a la conferencia
AN - SCOPUS:85075751605
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Applications of Machine Learning
A2 - Zelinski, Michael E.
A2 - Taha, Tarek M.
A2 - Howe, Jonathan
A2 - Awwal, Abdul A. S.
A2 - Iftekharuddin, Khan M.
PB - SPIE
T2 - Applications of Machine Learning 2019
Y2 - 13 August 2019 through 14 August 2019
ER -