The DAily Home LIfe Activity Dataset: A High Semantic Activity Dataset for Online Recognition

Abstract : 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.
Document type :
Conference papers
Complete list of metadatas

https://hal-cea.archives-ouvertes.fr/cea-01841019
Contributor : Léna Le Roy <>
Submitted on : Tuesday, July 17, 2018 - 7:58:39 AM
Last modification on : Thursday, March 21, 2019 - 2:16:32 PM

Identifiers

Citation

G. Vaquette, A. Orcesi, L. Lucat, C. Achard. The DAily Home LIfe Activity Dataset: A High Semantic Activity Dataset for Online Recognition. 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), May 2017, Washington, DC, United States. pp.497-504, ⟨10.1109/FG.2017.67⟩. ⟨cea-01841019⟩

Share

Metrics

Record views

167