Egok360: A 360 Egocentric Kinetic Human Activity Video Dataset
| Autor: | Yan Yan, Keshav Bhandari, Mario A. DeLaGarza, Ziliang Zong, Hugo Latapie |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Information retrieval Computer science Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Information and Computer Science Wearable computer 02 engineering and technology 010501 environmental sciences 01 natural sciences Field (computer science) Domain (software engineering) Activity recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0105 earth and related environmental sciences |
| Zdroj: | ICIP |
| Popis: | Recently, there has been a growing interest in wearable sensors which provides new research perspectives for 360 {\deg} video analysis. However, the lack of 360 {\deg} datasets in literature hinders the research in this field. To bridge this gap, in this paper we propose a novel Egocentric (first-person) 360{\deg} Kinetic human activity video dataset (EgoK360). The EgoK360 dataset contains annotations of human activity with different sub-actions, e.g., activity Ping-Pong with four sub-actions which are pickup-ball, hit, bounce-ball and serve. To the best of our knowledge, EgoK360 is the first dataset in the domain of first-person activity recognition with a 360{\deg} environmental setup, which will facilitate the egocentric 360 {\deg} video understanding. We provide experimental results and comprehensive analysis of variants of the two-stream network for 360 egocentric activity recognition. The EgoK360 dataset can be downloaded from https://egok360.github.io/. Comment: 5 pages, 5 figures, 1 table, 2020 IEEE International Conference on Image Processing (ICIP) |
| Databáze: | OpenAIRE |
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