Enhancing Surveillance Efficiency Through CCTV Footage Summarization

Abstract

The rapid growth of digital video content across platforms such as surveillance systems, social media, and multimedia repositories has created a strong demand for efficient video analysis and summarization techniques. Processing long-duration videos manually is time-consuming, computationally expensive, and prone to redundancy. To address these challenges, this research presents an automated video keyframe extraction framework based on deep neural network–driven shot segmentation and frame memorability analysis.The proposed system leverages pre-trained AlexNet models implemented using OpenCV’s Deep Neural Network (DNN) module to extract high-level visual features from video frames. Shot boundary detection is performed by computing the Euclidean distance between feature vectors of consecutive frames, enabling accurate identification of significant scene transitions. A threshold-based mechanism is employed to segment the video into meaningful shots. Within each detected shot, a memorability prediction model evaluates frames and assigns memorability scores, allowing the system to select the most representative and visually significant frame as the keyframe.The framework processes video inputs in standard formats such as MP4, AVI, and FLV, and dynamically analyzes frame sequences based on the video’s frame rate. The extracted keyframes are automatically stored for further inspection and downstream applications. By combining deep feature extraction with memorability-based ranking, the system effectively reduces redundancy while preserving important semantic content.Experimental observations indicate that the proposed approach successfully identifies meaningful keyframes across diverse video scenes, improving video summarization efficiency without compromising content relevance. The automated pipeline minimizes manual intervention and enhances scalability, making it suitable for applications including video indexing, surveillance monitoring, content-based video retrieval, and multimedia data management. Future enhancements may include adaptive thresholding, multi-model fusion, and real-time processing to further improve robustness and performance.