Suspicious Human Activity Recognition From Surveillance Videos Using Deep Learning

Suspicious Human activity recognition (SHAR) is crucial for improving surveillance and security systems by recognizing and reducing possible hazards in different situations. This study focuses on the task of precisely identifying potentially suspicious human behaviour by utilizing an innovative approach that harnesses advanced deep learning methods. Despite the abundance of research on the subject of SHAR, current methods frequently need to be revised with restricted levels of precision and efficiency. This research aims to address these constraints by presenting a thorough methodology for detecting and recognizing suspicious human activities. By rigorously collecting and preparing data, as well as training models, we aim to solve the issue of inaccurate and inefficient activity recognition in surveillance systems. By utilizing Convolutional Neural Networks (CNNs) and deep learning structures, such as the proposed time-distributed CNN model and Conv3D model, we attain notably enhanced accuracy rates of 90.14% and 88.23%, respectively, surpassing current research approaches. Moreover, the efficacy of our approach is illustrated by conducting prediction experiments on previously unreported test data and YouTube videos. Through the process of evaluating the trained models on unseen test data, we ascertain their accuracy and ability to apply learned knowledge to new situations. Moreover, the algorithms are utilized to predict dubious human conduct in a YouTube video, demonstrating their practical usefulness in real-life surveillance situations. The results of this study have important consequences for improving surveillance and security systems, allowing for better identification and reduction of possible dangers in various settings. Our methodology enhances the precision and effectiveness of SHAR, advancing the construction of more resilient and dependable surveillance systems and ultimately strengthening public safety and security.