Revolutionizing Anomaly Detection in Surveillance Footage with ARLSTM

Abstract

Video surveillance is an essential tool in maintaining public safety, security, and protecting assets. However, monitoring surveillance footage continuously for extended periods is challenging, and even the most vigilant human operators can miss anomalies or unusual activities that could pose a threat. Automatic Schemes are required to identify abnormalities in surveillance footage. Anomaly detection in surveillance footage is a thought-provoking problematic due to the high dimensionality and complexity of the data. Newly, deep learning-based methods have exposed promising results in detecting anomalies in video streams. Specifically, Long Short-Term Memory (LSTM) networks have been extensively used for 0mold temporal dependencies in video data. This paper proposes a novel approach for effective anomaly detection in surveillance footage using Attention Residual Long Short-Term Memory (ARLSTM) networks. The proposed ARLSTM architecture integrates attention mechanisms, residual connections, and LSTMs to improve the detection accuracy of anomalies in surveillance footage. The attention mechanism enables the network to selectively focus on relevant features in the data, while residual connections facilitate the propagation of gradients and alleviate the vanishing gradient problem. Furthermore, the LSTM component allows the network to capture temporal dependencies and model long-term patterns in the data. Investigational outcomes on standard datasets demonstrate that the proposed ARLSTM approach outperforms existing state-of-the-art methods for irregularity discovery in investigation footage. The proposed approach has potential applications in various domains, including public safety, security, and industrial automation. The proposed ARLSTM approach offers a robust and effective solution for detecting anomalies in surveillance footage. This work demonstrates the potential of deep learning-based approaches for automatic video surveillance and overlays the technique for future research in this field.