Spatiotemporal neural networks for action recognition based on joint loss

Abstract

Action recognition is a challenging and important problem in a myriad of significant fields, such as intelligent robots and video surveillance. In recent years, deep learning and neural network techniques have been widely applied to action recognition and attained remarkable results. However, it is still a difficult task to recognize actions in complicated scenes, such as various illumination conditions, similar motions, and background noise. In this paper, we present a spatiotemporal neural network model with a joint loss to recognize human actions from videos. This spatiotemporal neural network is comprised of two key connected substructures. The first one is a two-stream-based network extracting optical flow and appearance features from each frame of videos, which characterizes the human actions of videos in spatial dimension. The second substructure is a group of Long Short-Term Memory structures following the spatial network, which describes the temporal and transition information in videos. This research effort presents a joint loss function for training the spatiotemporal neural network model. By introducing the loss function, the action recognition performance is improved. The proposed method was tested with video samples from two challenging datasets. The experiments demonstrate that our approach outperforms the baseline comparison methods.