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

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

INTELLIGENT COMPUTER VISION SYSTEM FOR SCORE DETECTION IN BASKETBALL

Development of an intelligent computer vision system for Smart IoT basketball training and entertainment includes the development of a range of various subsystems, where score detection subsystem is playing a crucial role. This paper proposes the architecture of such a score detection subsystem to improve reliability and accuracy of the RFID technology used primarily for verification purposes. Challenges encompass both hardware-software interdependencies, optimal camera selection, and cost-effectiveness considerations. Leveraging machine learning algorithms, the vision-based subsystem aims not only to detect scores but also to facilitate online video streaming. Although the use of multiple cameras offers expanded field coverage and heightened precision, it concurrently introduces technical intricacies and increased costs due to image fusion and escalated processing requirements. This research navigates the intricate balance between achieving precise score detection and pragmatic system development. Through precise camera configuration optimization, the proposed system harmonizes hardware and software components