AI-DRIVEN VIDEO SUMMARIZATION AND SCENE RECOGNITION

This research introduces an advanced and unified approach for intelligent video segmentation that seamlessly integrates key-frame extraction with object-based analysis to achieve higher precision and computational efficiency in video interpretation.Unlike traditional methods that treat shot segmentation and object detection as independent stages, the proposed framework combines both processes within a single probabilistic and statistical learning model.The system utilizes Gaussian Mixture Models (GMM) trained through the Expectation-Maximization (EM) algorithm to effectively capture significant spatial and temporal visual patterns.Initially, key-frames are extracted to represent the most informative and dynamic portions of the video, thereby minimizing data redundancy while preserving essential content.These selected frames are then used to train the segmentation model, which classifies video regions into moving and static objects with enhanced accuracy.By integrating key-frame selection with object-based clustering, the approach not only reduces computational complexity but also improves robustness against noise and data inconsistencies.Experimental outcomes confirm that the proposed method delivers superior results in tasks such as motion object detection, video summarization, editing, and surveillance-based analytics.Overall, this integrated framework provides a compact, reliable, and scalable solution for advanced content-based video representation and analysis.