  Prediction of the action outcome is a new challenge for a robot
collaboratively working with humans. With the impressive progress in video
action recognition in recent years, fine-grained action recognition from video
data turns into a new concern. Fine-grained action recognition detects subtle
differences of actions in more specific granularity and is significant in many
fields such as human-robot interaction, intelligent traffic management, sports
training, health caring. Considering that the different outcomes are closely
connected to the subtle differences in actions, fine-grained action recognition
is a practical method for action outcome prediction. In this paper, we explore
the performance of convolutional gate recurrent unit (ConvGRU) method on a
fine-grained action recognition tasks: predicting outcomes of ball-pitching.
Based on sequences of RGB images of human actions, the proposed approach
achieved the performance of 79.17% accuracy, which exceeds the current
state-of-the-art result. We also compared different network implementations and
showed the influence of different image sampling methods, different fusion
methods and pre-training, etc. Finally, we discussed the advantages and
limitations of ConvGRU in such action outcome prediction and fine-grained
action recognition tasks.
