INTELLIGENT VIDEO SUMMARIZER

In recent years, there has been an explosive growth in digital video content generated from surveillance systems, social media platforms, and multimedia applications. Managing, analyzing, and retrieving meaningful information from such massive video datasets has become a major challenge for researchers and industries alike. Traditional manual analysis of long-duration videos is not only time-consuming but also prone to human error. Hence, there is an urgent need for an intelligent and automated mechanism that can summarize video content effectively without losing important contextual information. The Intelligent Video Summarizer proposed in this study addresses this challenge by applying advanced computer vision and deep learning techniques to automatically extract keyframes and generate concise summaries from lengthy video sequences. The system primarily focuses on scene boundary detection, feature extraction, and keyframe selection using convolutional neural networks (CNNs) and statistical correlation measures. These techniques enable the system to identify significant scene transitions, visual variations, and motion cues that contribute to meaningful storytelling within the video