Learning to anticipate and forecast human actions from videos
Action Anticipation and forecasting aims to predict future actions by processing videos containing past and current observations. In this project, we develop new methods based on the encoder-decoder architecture with Transformer models to anticipate and forecast future human actions by proce...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2022
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/158618 |
| الوسوم: |
إضافة وسم
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| المؤسسة: | Nanyang Technological University |
| اللغة: | English |
| الملخص: | Action Anticipation and forecasting aims to predict future actions by processing
videos containing past and current observations.
In this project, we develop new methods based on the encoder-decoder architecture
with Transformer models to anticipate and forecast future human actions by
processing videos. The model will observe a video for several seconds (or minutes)
and then encodes information of the video to predict plausible human action that are
going to happen in the future. Temporal information from videos will be extracted
from deep neural networks. The performance of these models will then be evaluated
on standard action forecasting datasets such as Breakfast and 50Salads datasets |
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