RGB+D and deep learning-based real-time detection of suspicious event in Bank-ATMs

摘要

Real-time detection of human activities has become very important in terms of surveillance and security of Bank-Automated Teller Machines (ATMs), public offices because of the day-to-day increase in criminal activities. The current way of monitoring such constrained environments is done through monocular CCTV cameras which capture only RGB video. The RGB+D sensor provides depth data of the scene in addition to RGB data. To address the problem of online detection of abnormal activities in Bank ATMs, we propose a supervised deep learning framework based on multi-stream CNNs and RGB+D sensor. From the online video stream of RGB+D data, motion templates are created from RGB and depth video segments and then trained on CNNs to detect a suspicious event in ongoing activity. Moreover, due to the unavailability of any dataset for analyzing human activities in ATMs, we also contributed a novel RGB+D dataset in this paper. The proposed deep learning-based framework is evaluated on qualitative and quantitative statistical evaluation parameters and detect suspicious event with the precision of 0.932 and accuracy of 94.2%. Detailed statistical analysis of results shows that the proposed framework can detect the suspicious event in a real-time online manner before the abnormal activity gets completed.