1. The DAily Home LIfe Activity Dataset: A High Semantic Activity Dataset for Online Recognition
- Author
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Astrid Orcesi, Geoffrey Vaquette, Laurent Lucat, Catherine Achard, Département Intelligence Ambiante et Systèmes Interactifs (DIASI), Laboratoire d'Intégration des Systèmes et des Technologies (LIST), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Institut des Systèmes Intelligents et de Robotique (ISIR), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), and Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA))
- Subjects
0209 industrial biotechnology ,Computer science ,On-line recognition ,Context (language use) ,02 engineering and technology ,computer.software_genre ,Semantics ,Facial recognition system ,Action recognition ,Life activity ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] ,Gesture recognition ,[SPI]Engineering Sciences [physics] ,020901 industrial engineering & automation ,0202 electrical engineering, electronic engineering, information engineering ,Face recognition ,ComputingMilieux_MISCELLANEOUS ,business.industry ,Smart homes ,Image recognition ,Subject (documents) ,Realistic conditions ,High level semantics ,Semantic levels ,020201 artificial intelligence & image processing ,Artificial intelligence ,Data mining ,business ,computer ,Natural language processing ,Home life - Abstract
Conference of 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 ; Conference Date: 30 May 2017 Through 3 June 2017; Conference Code:128713; International audience; In this article, we introduce the DAily Home LIfe Activity (DAHLIA) Dataset, a new dataset adapted to the context of smart-home or video-assistance. Videos were recorded in realistic conditions, with 3 KinectTMv2 sensors located as they would be in a real context. The very long-range activities were performed in an unconstrained way (participants received few instructions), and in a continuous (untrimmed) sequence, resulting in long videos (39 min in average per subject). Contrary to previously published databases, in which labeled actions are very short and with low-semantic level, this new database focuses on high-level semantic activities such as 'Preparing lunch' or 'House Working'. As a baseline, we evaluated several metrics on three different algorithms designed for online action recognition or detection.
- Published
- 2017