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.

由于犯罪活动的日益增加，实时检测人类活动在银行自动柜员机 (ATM)、公共办公室的监视和安全方面变得非常重要。当前监控此类受限环境的方法是通过仅捕获 RGB 视频的单目闭路电视摄像机完成的。除了 RGB 数据之外，RGB+D 传感器还提供场景的深度数据。为了解决银行 ATM 异常活动的在线检测问题，我们提出了一种基于多流 CNN 和 RGB+D 传感器的监督深度学习框架。从 RGB+D 数据的在线视频流中，根据 RGB 和深度视频片段创建运动模板，然后在 CNN 上进行训练，以检测正在进行的活动中的可疑事件。而且，由于没有任何数据集可用于分析 ATM 中的人类活动，我们还在本文中贡献了一个新的 RGB+D 数据集。所提出的基于深度学习的框架对定性和定量统计评估参数进行评估，并以 0.932 的精度和 94.2% 的准确度检测可疑事件。结果的详细统计分析表明，所提出的框架可以在异常活动完成之前以实时在线方式检测可疑事件。