A Comprehensive Study on Advanced Video Data Preprocessing Techniques for Enhanced Object Detection

Video processing has become a vital area in computer vision and deep learning, with diverse applications including crowd analysis, anomaly identification, and activity tracking. Although numerous surveys have examined various aspects of these functionalities, there is still a requirement for a complete review that combines these findings into a coherent perspective. This survey study provides a comprehensive analysis of several model architectures, emphasising their advantages, shortcomings, and constraints. We also emphasise the profound influence of these technologies in several fields, such as surveillance, healthcare, and autonomous systems, specifically focussing on the applications of deep learning in video processing. Our review not only analyses the latest advancements but also explores the complex processes and tactics used by deep learning models to derive valuable insights from video data. Furthermore, we examine the importance of accessible datasets and their crucial role in propelling research progress in this field. By outlining the obstacles and concerns that researchers have while adopting these systems, we offer a clear plan for future research paths. We want to stimulate ongoing innovation and advancement in the domain of video processing using deep learning techniques.