Name
Abstract | ||
|---|---|---|
The online detection of action start in video data has witnessed an increase in attention from both academia and industry, for abundant use-cases (e.g., an alert mechanism in videos used for surveillance with an ability to automate the recording of key frames and timestamp). Conventional approaches heavily rely on frame-level annotations and other prior knowledge that can only be applied to limited categories. In this paper, we introduce Generic Action Start Detection (GASD): a new task that aims to detect the taxonomy-free action start in an online manner. Further-more, one novel yet simple design, 3D MLP-mixer based architecture with a multiscaled sampling training strategy, is proposed, which makes the GASD algorithm favorable for edge-device deployment. The GASD task is validated on two large-scale datasets, THUMOS'14 and ActivityNet1.2. Results demonstrate that the proposed architecture achieves the SOTA performance on the GASD task compared with other online action start detection algorithms. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/MIPR54900.2022.00074 | 2022 IEEE 5th International Conference on Multimedia Information Processing and Retrieval (MIPR) |
Keywords | DocType | ISSN |
generic,action start detection,online | Conference | 2770-4327 |
ISBN | Citations | PageRank |
978-1-6654-9549-3 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuexi Zhang | 1 | 0 | 0.34 |
| Ming Chen | 2 | 0 | 0.34 |
| Yikang Li | 3 | 0 | 0.34 |
| Jenhao Hsiao | 4 | 2 | 1.42 |
| Octavia Camps | 5 | 0 | 0.34 |
| Chiuman Ho | 6 | 2 | 1.42 |