Real-Time Conceptual Video Interpretation for Surveillance Systems using Euclidean Norms

Information retrieval is intended to help people who are constantly looking for information. Partitioning the video into frames is the first stage in video information retrieval. Most video frames are brief and do not provide much information about the image content. On the other hand, scene border recognition or video fragmentation into scenes provides a better understanding of the video scene by clustering images based on similar image content. This paper is about video scene identification, specifically video formation mining for template matching with deep characteristics. The study proposed and created a workflow that included phases for frame extraction, finding similarities between consecutive frames, grouping frames, identifying key frames, and seeing detection by merging the relevant frames. Python's OpenCV generates the frames. The process is evaluated using scene identification metrics. The results show that scene detection and quality are significant, as measured by several criteria. In addition, we examined and studied current recognition and analysis criteria. Furthermore, our proposed methodologies have been thoroughly tested on various public scene video datasets, and they outperform some state-of-the-art approaches. This work's findings can be used to create real-time conceptual video interpretations.