@inproceedings{fd5247a8b4ef4581ab36d4dbe762781c,
title = "Multi-View and Multi-Modal Action Recognition with Learned Fusion",
abstract = "In this paper, we study multi-modal and multi-view action recognition system based on the deep-learning techniques. We extended the Temporal Segment Network with additional data fusion stage to combine information from different sources. In this research, we use multiple types of information from different modality such as RGB, depth, infrared data to detect predefined human actions. We tested various combinations of these data sources to examine their impact on the final detection accuracy. We designed 3 information fusion methods to generate the final decision. The most interested one is the Learned Fusion Net designed by us. It turns out the Learned Fusion structure has the best results but requires more training.",
keywords = "deep learning, human action recognition, information fusion, multi-modal video, multi-view video, neural nets",
author = "Sandy Ardianto and Hsueh-Ming Hang",
year = "2019",
month = mar,
day = "4",
doi = "10.23919/APSIPA.2018.8659539",
language = "English",
series = "2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1601--1604",
booktitle = "2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings",
address = "United States",
note = "null ; Conference date: 12-11-2018 Through 15-11-2018",
}