What and How? Jointly Forecasting Human Action and Pose
| Autor: | Zhang Yanxia, Andreas Girgensohn, David Doermann, Yanjun Zhu, Qiong Liu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Sequence
business.industry Computer science 05 social sciences 050301 education 02 engineering and technology Motion (physics) Task (project management) Range (mathematics) Action (philosophy) Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering Task analysis Trajectory 020201 artificial intelligence & image processing Artificial intelligence business 0503 education |
| Zdroj: | ICPR |
| DOI: | 10.1109/icpr48806.2021.9412833 |
| Popis: | Forecasting human actions and motion trajectories address the problem of predicting what a person is going to do next and how they will perform it. This is crucial in a wide range of applications, such as assisted living and future co-robotic settings. We propose to simultaneously learn actions and action-related human motion dynamics while existing works perform them independently. This paper presents a method to jointly forecast categories of human action and skeletal joint pose, allowing the two tasks to reinforce each other. As a result, our system can predict future actions and the motion trajectories that will result. To achieve this, we define a task of joint action classification and pose regression. We employ a sequence to sequence encoder-decoder model combined with multi-task learning to forecast future actions and poses progressively before the action happens. Experimental results on two public datasets, IkeaDB and OAD, demonstrate the effectiveness of the proposed method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|