Video Classification-Based Action Recognition with Enhanced Convolutional Neural Networks

Abstract

The classification of videos has become increasingly important in the field of data science research, as it has numerous practical applications in modern society. Compared to image classification, video classification poses a significantly greater challenge. One of the most obvious difficulties is that video classification tasks require more powerful computers due to the large number of features that need to be computed. Additionally, conventional 2D Convolutional Neural Networks (2D CNNs) are not effective in handling such tasks. This paper proposes a novel 2-layer Convolutional Neural Network (CNN) architecture for action recognition that addresses these challenges. The proposed architecture achieved a high test accuracy of 79.66% for classifying large video clips. The results indicate the effectiveness of the proposed approach for video classification tasks.