Two-stream deep representation for human action recognition
Najla Bouarada Ghrab, Emna Fendri, Mohamed Hammami · 2022
Human action recognition has received a lot of attention in computer vision community given its interest in many real applications. In this paper, we proposed a new method for human action recognition based on deep learning methods. The main contribution of the proposed method is an efficient combination of two Convolutional neural networks. The two-stream framework allows to fully utilize the rich multimodal information in videos. In fact, we explored the complementarity between appearance information and motion information to represent human actions. Specifically, we suggested a spatial Convolutional Neural Network performed on still individual images to model spatial information. To exploit motion between frames, a second Convolutional Neural Network is processed on accumulated optical flow images obtained by stacking the optical flow estimations between consecutive frames in a single image. Then, a fusion score is performed between the two Convolutional Neural Networks to achieve the appropriate class. In order to prove the performance of our method, we trained and evaluated our architecture on a standard human actions benchmark, the Weizmann dataset.