Deep Neural Network Approach for Real-Time Abnormal Activity and Crime Detection in Surveillance Videos through Video Summarization - Ghode, Deepak; Jose T, Anto Pratheesh; Jain, Meenakshi; Aggarwal, Divya; Sood, Sunny; Sharma, Urvashi (International Journal of Aquatic Research and Environmental Studies, 2026)
The exponential growth of surveillance video data in modern smart city infrastructure has rendered manual video monitoring impractical and error-prone. Video summarization is the task of condensing lengthy surveillance footage into compact, informative representations has emerged as a critical solution. This paper presents a novel video summarization framework driven by the concept of abnormal activity detection, wherein only frames containing behaviorally anomalous events are retained in the final summary. The proposed system integrates a Convolutional Neural Network (CNN) feature extractor, a Long Short-Term Memory (LSTM) sequence model, and an attention-guided scoring mechanism to identify temporal segments of interest. An unsupervised Gaussian Mixture Model (GMM) is employed to model normal activity patterns, enabling detection of statistical deviations that signal abnormal behavior. The framework is evaluated on the UCSD Anomaly Detection, UCF-Crime, and ShanghaiTech Campus datasets, achieving a frame-level detection AUC of 92.4% and a summarization compression ratio of 87.3% while retaining 96.1% of ground-truth abnormal events. Comparative experiments demonstrate significant improvements over baseline key-frame and unsupervised summarization methods. The results confirm that abnormality-centric summarization produces semantically richer and forensically more actionable video summaries than purely aesthetic or redundancy-driven approaches.