HUMAN ACTION DETECTION

Human action recognition is an important yet challenging research topic in the computer vision community. In this paper, we propose context features along with a deep model to recognize the individual subject action in the videos of real-world scenes. Besides the motion features of the subject, we also utilize context information from multiple sources to improve the recognition performance. We introduce the scene context features that describe the environment of the subject at global and local levels. We design a deep neural network structure to obtain the high-level representation of human action combining both motion features and context features. We demonstrate that the proposed context feature and deep model improve the action recognition performance by comparing with baseline approaches. We also show that our approach outperforms state-of-the-art methods on 5-activities and 6-activities versions of the Collective Activities Dataset.