Video Summarization Using Object Detection Method

Video summarization plays an important role in managing the rapidly growing volume of digital video content, helping users understand and review long recordings without watching every frame. In this work, we present a video summarization approach that integrates object detection to better capture meaningful scenes and events. Instead of relying only on visual features like color or motion, our method uses deep learning-based object detection models to identify important objects and activities within each frame. These detected elements guide the summarization process, allowing the system to select frames and segments that truly represent the essence of the video. By focusing on content that carries semantic significance, the generated summaries become more informative and context-aware. The approach not only reduces redundancy but also improves clarity and user engagement. This method can be particularly useful in areas such as surveillance monitoring, video archiving, online media platforms, and recommendation systems, where quick understanding of video content is essential. Overall, the proposed system offers a practical and intelligent way to condense videos while preserving their core meaning.