AI-Enabled Video Frame Segmentation for Specific Person Identification

The applications of Computer Vision (CV) in video analysis offers an efficient solution for the real-world challenges, especially in the surveillance and monitoring tasks. This project presents a deep learning-based approach for video frame segmentation, aimed at accurately identifying and retrieving specific time frames of an individual's presence in the long-duration videos. For this, three primary frameworks have been employed: YOLO (You Only Look Once) for detecting individual presence in a video; DeepSORT algorithm for continuous and seamless person tracking throughout the video; ArcFace for advanced facial recognition of the targeted individual. The proposed system ensures precise identification and smooth tracking, eliminating the need for time-consuming manual searches and significantly enhancing the efficiency of the video analysis tasks. Developed using the PyTorch framework, this video analytics system provides a reliable and user-friendly mechanism for retrieving exact time frames where an individual appears in the video. This system holds considerable promise for improvising the security systems and smart surveillance by reliable detection and tracking of individuals within the large volumes of video footage.