Natha Sarfaraz, Ahmed Fareed, Siraj Mohammad, Lagari Mehwish, Altamimi Majid, Chandio Asghar Ali
Department of Information Technology, Quaid e Awam University, Nawabshah 67450, Pakistan.
Department of Software Engineering, Sir Syed University of Engineering & Technology, Karachi 75300, Pakistan.
Sensors (Basel). 2025 Jan 4;25(1):251. doi: 10.3390/s25010251.
Detection of anomalies in video surveillance plays a key role in ensuring the safety and security of public spaces. The number of surveillance cameras is growing, making it harder to monitor them manually. So, automated systems are needed. This change increases the demand for automated systems that detect abnormal events or anomalies, such as road accidents, fighting, snatching, car fires, and explosions in real-time. These systems improve detection accuracy, minimize human error, and make security operations more efficient. In this study, we proposed the Composite Recurrent Bi-Attention (CRBA) model for detecting anomalies in surveillance videos. The CRBA model combines DenseNet201 for robust spatial feature extraction with BiLSTM networks that capture temporal dependencies across video frames. A multi-attention mechanism was also incorporated to direct the model's focus to critical spatiotemporal regions. This improves the system's ability to distinguish between normal and abnormal behaviors. By integrating these methodologies, the CRBA model improves the detection and classification of anomalies in surveillance videos, effectively addressing both spatial and temporal challenges. Experimental assessments demonstrate that the CRBA model achieves high accuracy on both the University of Central Florida (UCF) and the newly developed Road Anomaly Dataset (RAD). This model enhances detection accuracy while also improving resource efficiency and minimizing response times in critical situations. These advantages make it an invaluable tool for public safety and security operations, where rapid and accurate responses are needed for maintaining safety.
视频监控中的异常检测在确保公共场所的安全方面起着关键作用。监控摄像头的数量不断增加，使得人工监控变得更加困难。因此，需要自动化系统。这种变化增加了对能够实时检测异常事件或异常情况（如道路事故、打架、抢夺、汽车火灾和爆炸）的自动化系统的需求。这些系统提高了检测准确性，最大限度地减少了人为错误，并使安全操作更加高效。在本研究中，我们提出了用于检测监控视频中异常情况的复合循环双注意力（CRBA）模型。CRBA模型将用于稳健空间特征提取的DenseNet201与捕获视频帧间时间依赖性的双向长短期记忆（BiLSTM）网络相结合。还引入了多注意力机制，以引导模型将注意力集中在关键的时空区域。这提高了系统区分正常和异常行为的能力。通过整合这些方法，CRBA模型改进了监控视频中异常情况的检测和分类，有效应对了空间和时间方面的挑战。实验评估表明，CRBA模型在中佛罗里达大学（UCF）数据集和新开发的道路异常数据集（RAD）上均取得了高精度。该模型提高了检测准确性，同时还提高了资源效率，并在关键情况下将响应时间降至最低。这些优势使其成为公共安全和安保行动中不可或缺的工具，在维护安全方面需要快速准确的响应。
