Name
Abstract | ||
|---|---|---|
Research on depth-based human activity analysis achieved outstanding performance and demonstrated the effectiveness of 3D representation for action recognition. The existing depth-based and RGB+D-based action recognition benchmarks have a number of limitations, including the lack of large-scale training samples, realistic number of distinct class categories, diversity in camera views, varied environmental conditions, and variety of human subjects. In this work, we introduce a large-scale dataset for RGB+D human action recognition, which is collected from 106 distinct subjects and contains more than 114 thousand video samples and 8 million frames. This dataset contains 120 different action classes including daily, mutual, and health-related activities. We evaluate the performance of a series of existing 3D activity analysis methods on this dataset, and show the advantage of applying deep learning methods for 3D-based human action recognition. Furthermore, we investigate a novel one-shot 3D activity recognition problem on our dataset, and a simple yet effective Action-Part Semantic Relevance-aware (APSR) framework is proposed for this task, which yields promising results for recognition of the novel action classes. We believe the introduction of this large-scale dataset will enable the community to apply, adapt, and develop various data-hungry learning techniques for depth-based and RGB+D-based human activity understanding. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TPAMI.2019.2916873 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
Algorithms,Benchmarking,Deep Learning,Human Activities,Humans,Image Processing, Computer-Assisted,Pattern Recognition, Automated,Semantics,Video Recording | Journal | 42 |
Issue | ISSN | Citations |
10 | 0162-8828 | 32 |
PageRank | References | Authors |
0.96 | 18 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Liu | 1 | 671 | 30.44 |
| Shahroudy, A. | 2 | 585 | 13.84 |
| Mauricio Perez | 3 | 67 | 7.64 |
| Gang Wang | 4 | 2869 | 135.49 |
| Ling-yu Duan | 5 | 1770 | 124.87 |
| Alex C. Kot | 6 | 1096 | 92.07 |