Advancements in Deep Learning for Smart Surveillance: A Survey on Crime and Anomaly Detection

The increasing prevalence of advanced surveillance technologies has spurred significant research in automated anomaly detection systems using deep learning. This survey synthesizes recent developments across various distinct studies addressing diverse security challenges. These include theft detection using Convolutional Neural Networks (CNN) with object tracking, robbery identification using convolutional LSTM models trained on the UNI-Crime dataset, anomaly detection leveraging weakly supervised learning on UCF-Crime data, and weapon recognition using YOLOv3. Several other approaches demonstrate significant accuracy and efficiency improvements, achieving accuracies. Despite varying datasets and architectures, the integration of CNNs, YOLO, and LSTM models reflects a shared emphasis on spatial-temporal feature extraction for real-time applications. This paper provides a comparative analysis of these methodologies, highlighting the role of dataset quality, transfer learning, and computational efficiency in advancing smart surveillance systems to mitigate crime and enhance public safety.