Learning Features for Human Action Recognition Using Multilayer Architectures

摘要

This paper presents an evaluation of two multilevel architectures in the human action recognition (HAR) task. By combining low level features with multi-layer learning architectures, we infer discriminative semantic features that highly improve the classification performance. This approach eliminates the difficult process of selecting good mid-level feature descriptors, changing the feature selection and extraction process by a learning stage. The data probability distribution is modeled by a multi-layer graphical model. In this way, this approach is different to the standard ones. Experiments on KTH and Weizmann video sequence databases are carried out in order to evaluate the performance of the proposal. The results show that the new learnt features offer a classification performance comparable to the state-of-the-art on these databases.