Dr. Najla Bouarada Ghrab Profile

Dr. Najla Bouarada Ghrab

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Proceedings Article | 5 March 2022 Paper

Two-stream deep representation for human action recognition

Najla Bouarada Ghrab, Emna Fendri, Mohamed Hammami

Proceedings Volume 12084, 1208410 (2022) https://doi.org/10.1117/12.2623121

KEYWORDS: Video, Optical flow, Convolutional neural networks, Feature extraction, Video surveillance, Motion models, Image fusion, RGB color model, Data modeling, Image processing

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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.

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