Anticipating Daily Intention using On-Wrist Motion Triggered Sensing
| Autor: | Chan-Wei Hu, Ting-An Chien, Cheng-Sheng Chan, Tz-Ying Wu, Min Sun |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
business.industry Computer science Computer Vision and Pattern Recognition (cs.CV) 05 social sciences Process (computing) Intelligent decision support system Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology computer.software_genre Motion (physics) Expert system Visualization Recurrent neural network Action (philosophy) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Computer vision Artificial intelligence business computer 050107 human factors |
| Zdroj: | ICCV |
| Popis: | Anticipating human intention by observing one’s actions has many applications. For instance, picking up a cellphone, then a charger (actions) implies that one wants to charge the cellphone (intention) (Fig. 1). By anticipating the intention, an intelligent system can guide the user to the closest power outlet. We propose an on-wrist motion triggered sensing system for anticipating daily intentions, where the on-wrist sensors help us to persistently observe one’s actions. The core of the system is a novel Recurrent Neural Network (RNN) and Policy Network (PN), where the RNN encodes visual and motion observation to anticipate intention, and the PN parsimoniously triggers the process of visual observation to reduce computation requirement. We jointly trained the whole network using policy gradient and cross-entropy loss. To evaluate, we collect the first daily “intention” dataset consisting of 2379 videos with 34 intentions and 164 unique action sequences (paths in Fig. 1). Our method achieves 92:68%; 90:85%; 97:56% accuracy on three users while processing only 29% of the visual observation on average. |
| Databáze: | OpenAIRE |
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