Action recognition by learning locally adaptive classifiers

In this paper, we propose a novel framework for video-based human action recognition, which can effectively resolve the difficulty caused by large variations within each action category. We first use the cloud of interest points to represent human action, due to its effectiveness in extracting spatio-temporal information necessary to reliability distinguish each action. Then, we perform an efficient local learning on the extracted features to learn locally adaptive classifiers. Specifically, a local classifier is specifically trained for each training sample. A local classifier could better describe the local data distribution and thus adopting multiple local classifiers would lead to better classification accuracy. We conduct several experiments on the KTH dataset and obtain very inspiring results. In particular, our approach achieves comparable performance to that of the state-of-the-art methods. Compared with a global learning method, i.e., the AdaBoost, the local learning provides significantly better accuracy with little additional cost in training time.