Designing a Deep Learning Model for Video Anomaly Detection-Based Surveillance

Abstract

Anomaly detection in surveillance videos is a crucial task for ensuring public safety and security. Traditional methods rely on rule-based or handcrafted feature-based approaches, which are often limited in their ability to detect complex and subtle anomalies. In recent years, deep learning models have shown promising results in detecting anomalies in video data. In this paper, we propose a deep learning model for video anomaly detection in surveillance videos. Our model utilizes a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The CNNs are used to extract spatial features from individual frames, while the LSTM networks are used to capture the temporal dependencies between frames. We propose to use a two-stage training process to train our model. In the first stage, we pretrain the CNNs on a large dataset of unlabeled images to learn generic features. In the second stage, we fine-tune the CNNs and train the LSTM networks on a smaller dataset of labeled surveillance videos. To evaluate our model, we will use the UCSD Pedestrian dataset, which contains video sequences of pedestrians walking in a busy street. We will compare our model's performance with proposed hybrid methods and demonstrate its ability to detect various anomalies, such as sudden changes in the number of pedestrians or the presence of unusually detected various anomalies in the scene. Our proposed model shows great potential for detecting anomalies in surveillance videos, which can greatly improve public safety and security in various settings.