Abstract
Anticipating human intentional actions is essential for many applications involving service robots and social robots. Nowadays assisting robots must do reasoning beyond the present with predicting future actions. It is difficult due to its non-Markovian property and the rich contextual information. This task requires the subtle details inherent in human movements that may imply a future action. This paper presents a probabilistic method for action prediction in human-object interactions. The key idea of our approach is the description of the so-called object affordance, the concept which allows us to deliver a trajectory visualizing a possible future action. Extensive experiments were conducted to show the effectiveness of our method in action prediction. For evaluation we applied a new RGB-D activity video dataset recorded by the Sez3D depth sensors. The dataset contains several human activities composed out of different actions.
Highlights
In everyday life a human performs various actions
We conducted an experimental evaluation comparing our method to other methods using: (a) the so-called “chance model” which randomly selects the time moments and makes the prediction for that time, we use its published code and followed the settings given in [17], (b) the method using Hidden Markov Model (HMM) in which the hidden state sequences corresponding to the observation is considered, (c) Linear Support Vector Machine (LSVM) method where the transitions between the actions are focused [42]
We presented the problem of human action prediction
Summary
Being able to detect and anticipate which action is going to be performed in a complex environment is important for assistive robots, social robots and healthcare assistants. Such ability requires reasoning tools and methods. With such capability [20], a robot is able to plan ahead with reactive responses together with avoiding potential accidents. When a partial observation is available, we should be able to predict what is going to happen (e.g., a person is about to open the door as shown in the Fig. 1). It is necessary that a reliable prediction is done at the early stage of an action, e.g., when only 60% of a whole action was observed
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