Intelligent Multi-Group Marine Predator Algorithm With Deep Learning Assisted Anomaly Detection in Pedestrian Walkways

Anomaly Detection (AD) in Pedestrian Walkways (PWs) is critical to urban security and safety systems. It is widely used to detect abnormal or unusual behaviours, situations, or events in areas dedicated to pedestrian traffic, namely crosswalks, sidewalks, or pedestrian bridges. The main objective is to improve efficiency, safety, and security in the urban environment by identifying deviations and monitoring pedestrian activities from established norms. This kind of AD typically includes surveillance cameras, sensors, and advanced software algorithms. Using advanced machine learning (ML) and computer vision (CV) approaches, this technique continuously monitors the pedestrian area to detect potential threats and irregularities. Deep Learning Assisted AD in Pedestrian Walkways presents a novel and very efficient method to enhance security and safety in urban environments. Therefore, this study designs an Intelligent Multi-Group Marine Predator Algorithm with Deep Learning Assisted Anomaly Detection (MMPADL-AD) in Pedestrian Walkways. The MMPADL-AD system aims to ensure security in PWs via the AD process. The MMPADL-AD technique incorporates a NASNet feature extractor that proficiently extracts high-level features from surveillance data, allowing a deep understanding of pedestrian behaviours. Besides, the MMPADL-AD technique applies convolutional long short-term memory (ConvLSTM), inheriting the benefits of convolutional neural networks) and LSTM for the AD process. Finally, the MMPA has been used for the hyperparameter tuning mechanism, which optimizes the model's performance, assuring accuracy and adaptability. Benchmark data accompanied an extensive set of experiments to ensure the higher effectiveness of the MMPADL-AD approach. The experimental values highlighted the supremacy of the MMPADL-AD approach over other DL methods.