Enhancing Human Activity Analysis in Video Surveillance with Recurrent Neural Networks

Abstract

Recently, numerous security systems have been implemented to enhance security in public and private spaces. However, relying solely on human monitoring of surveillance cameras can lead to errors and missed events, making it inefficient and time-consuming. To address this issue, this research explored the effectiveness of recurrent models in investigations involving sequences, as deep convolutional network models have primarily dominated image interpretation challenges. This study developed a group of end-to-end trainable, deep, and task-specific recurrent convolutional architectures for visual understanding. These models excelled in detecting human activities and were utilized to create a model specifically designed for identifying unusual events in security camera data. This study approach employed a Convolutional Neural Network to extract important features from each frame in the input sequence. Additionally, this study implemented a classification mechanism capable of distinguishing between common and abnormal behaviors, enabling the system to categorize each detected aberration accurately. To evaluate the performance of the proposed model, this study utilized the UCF50 dataset and achieved an impressive accuracy of approximately 93%. This accuracy surpassed other models, such as ConLSTM, when tested on the same dataset.