A Deep Learning Approach to Video Classification for Indoor and Outdoor Environments

Abstract

This research paper explores the application of deep learning techniques for video classification, specifically focusing on distinguishing between indoor and outdoor environments. We present a comprehensive analysis of different deep learning models and methodologies used for this classification task, evaluating their performance and effectiveness. Our study includes a detailed exploration of feature extraction methods, model architectures, and training strategies tailored to indoor-outdoor video classification. Through extensive experimentation and evaluation on benchmark datasets, we demonstrate the efficacy of our proposed approach, achieving significant accuracy rates and outperforming existing methods in this domain. The findings from this research contribute valuable insights and advancements in video classification using deep learning, with potential applications in various real-world scenarios such as surveillance, robotics, and environmental monitoring.