期刊
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
卷 14, 期 4, 页码 1-12出版社
SAGE PUBLICATIONS INC
DOI: 10.1177/1729881417709079
关键词
Human daily activity recognition; public RGB-D data sets merging; large-scale RGB-D activity data set; depth motion maps; depth cuboid similarity feature; curvature space scale
类别
资金
- National Hightech Research and Development Program 863, China [2015AA042307]
- Shandong Provincial Scientific and Technological Development Foundation, China [2014GGX103038]
- Shandong Provincial Independent Innovation AMP
- Achievement Transformation Special Foundation, China [2015ZDXX0101E01]
- Fundamental Research Funds of Shandong University, China [2015JC027]
Human daily activity recognition has been a hot spot in the field of computer vision for many decades. Despite best efforts, activity recognition in naturally uncontrolled settings remains a challenging problem. Recently, by being able to perceive depth and visual cues simultaneously, RGB-D cameras greatly boost the performance of activity recognition. However, due to some practical difficulties, the publicly available RGB-D data sets are not sufficiently large for benchmarking when considering the diversity of their activities, subjects, and background. This severely affects the applicability of complicated learning-based recognition approaches. To address the issue, this article provides a large-scale RGB-D activity data set by merging five public RGB-D data sets that differ from each other on many aspects such as length of actions, nationality of subjects, or camera angles. This data set comprises 4528 samples depicting 7 action categories (up to 46 subcategories) performed by 74 subjects. To verify the challengeness of the data set, three feature representation methods are evaluated, which are depth motion maps, spatiotemporal depth cuboid similarity feature, and curvature space scale. Results show that the merged large-scale data set is more realistic and challenging and therefore more suitable for benchmarking.
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