Activity Recognition Using Deep Learning for Video Surveillance

Abstract

Contemporary security frameworks have progressively integrated automated surveillance systems to oversee both public and private settings. Conventional methods that depend solely on human operators for video surveillance are susceptible to errors and inefficiencies, resulting in significant time and resource expenditures. This study explored the capabilities of sequential modeling architectures for time-series analysis, given that deep convolutional frameworks have primarily focused on static image interpretation tasks. The study created extensive, end-toend, trainable deep architectures featuring task-specific recurrent convolutional structures for visual understanding. We utilized these frameworks, which demonstrated enhanced efficacy in human behavior detection, to develop a specific model for anomaly detection in surveillance footage. The methodology utilized Convolutional Neural Networks for feature extraction from sequential frame inputs. The study established a classification system that distinguishes between normal and abnormal actions, facilitating accurate categorization of identified anomalies. The performance assessment utilizing the UCF50 dataset achieved remarkable accuracy of around 93%. This performance surpassed other methods, including ConLSTM, when assessed on the same datasets.