Human intention understanding based on object affordance and action classification

Intention understanding is a basic requirement for human-machine interaction. Action classification and object affordance recognition are two possible ways to understand human intention. In this study, Multiple Timescale Recurrent Neural Network (MTRNN) is adapted to analyze human action. Supervised MTRNN, which is an extension of Continuous Timescale Recurrent Neural Network (CTRNN), is used for action and intention classification. On the other hand, deep learning algorithms proved to be efficient in understanding complex concepts in complex real world environment. Stacked denoising auto-encoder (SDA) is used to extract human implicit intention related information from the observed objects. A feature based object detection method namely Speeded Up Robust Features (SURF) is also used to find the object information. Object affordance describes the interactions between agent and the environment. In this paper, we propose an intention recognition system using `action classification´ and `object affordance information´. Experimental result shows that supervised MTRNN is able to use different information in different time period and improve the intention recognition rate by cooperating with the SDA.