Abstract
In this paper, we propose a robust online action recognition method based on boosted sequential classification. Our method utilizes boosting algorithm that is one of ensemble learning algorithms. This algorithm is also known as a feautre selector and has been utilized in the fields of image processing and natural language processing in recent years. Based on the boosting scheme, our method can automatically and efficiently select significant features for action recognition. Additionally, the method leverages temporal dependency of actions based on Ising model to improve recognition performance. We evaluated our method to action recognition, such as walking and running, using motion capture data only with posture features. In the result, our method can classify the actions more robustly than the method that does not utilize temporal dependency of actions.
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