Smart surveillance systems are used to monitor specific areas, such as homes, buildings, and borders, and these systems can effectively detect any threats. In this work, we investigate the design of low-cost multiunit surveillance systems that can control numerous surveillance cameras to track multiple objects (i.e., people, cars, and guns) and promptly detect human activity in real time using low computational systems, such as compact or single board computers. Deep learning techniques are employed to detect certain objects to surveil homes/buildings and recognize suspicious and vital events to ensure that the system can alarm officers of relevant events, such as stranger intrusions, the presence of guns, suspicious movements, and identified fugitives. The proposed model is tested on two computational systems, specifically, a single board computer (Raspberry Pi) with the Raspbian OS and a compact computer (Intel NUC) with the Windows OS. In both systems, we employ components, such as a camera to stream real-time video and an ultrasonic sensor to alarm personnel of threats when movement is detected in restricted areas or near walls. The system program is coded in Python, and a convolutional neural network (CNN) is used to perform recognition. The program is optimized by using a foreground object detection algorithm to improve recognition in terms of both accuracy and speed. The saliency algorithm is used to slice certain required objects from scenes, such as humans, cars, and airplanes. In this regard, two saliency algorithms, based on local and global patch saliency detection are considered. We develop a system that combines two saliency approaches and recognizes the features extracted using these saliency techniques with a conventional neural network. The field results demonstrate a significant improvement in detection, ranging between 34% and 99.9% for different situations. The low percentage is related to the presence of unclear objects or activities that are different from those involving humans. However, even in the case of low accuracy, recognition and threat identification are performed with an accuracy of 100% in approximately 0.7 s, even when using computer systems with relatively weak hardware specifications, such as a single board computer (Raspberry Pi). These results prove that the proposed system can be practically used to design a low-cost and intelligent security and tracking system.

中文翻译：

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基于快速CNN的低成本智能监控系统

智能监视系统用于监视特定区域，例如房屋，建筑物和边界，并且这些系统可以有效地检测任何威胁。在这项工作中，我们研究了低成本多单元监视系统的设计，该系统可以控制多个监视摄像机来跟踪多个物体（例如人，汽车和枪支），并使用低计算量系统（例如，紧凑型或单板计算机。深度学习技术用于检测监视房屋/建筑物的某些物体并识别可疑和重要事件，以确保系统可以向警官发出相关事件的警报，例如陌生人入侵，枪支存在，可疑活动和已识别的逃犯。所提出的模型在两个计算系统上进行了测试，具体而言，具有Raspbian操作系统的单板计算机（Raspberry Pi）和具有Windows OS的紧凑型计算机（Intel NUC）。在这两个系统中，我们都使用了组件，例如摄像机以传输实时视频流，以及超声波传感器，以在限制区域或墙壁附近检测到移动时向人员发出威胁警报。该系统程序使用Python进行编码，并且使用卷积神经网络（CNN）进行识别。通过使用前景物体检测算法对程序进行优化，以提高准确性和速度方面的识别能力。显着性算法用于从场景中切出某些所需对象，例如人，汽车和飞机。在这方面，考虑了基于局部和全局补丁显着性检测的两种显着性算法。我们开发了一种系统，该系统结合了两种显着性方法，并且可以识别使用这些显着性技术与常规神经网络提取的特征。现场结果表明，在不同情况下，检测效果显着提高，介于34％和99.9％之间。较低的百分比与存在与人类不同的不清楚的物体或活动有关。但是，即使在精度较低的情况下，即使在使用硬件规格相对较弱的计算机系统（例如单板计算机（Raspberry Pi））时，也可以在约0.7 s内以100％的精度执行识别和威胁识别。这些结果证明，所提出的系统可以实际用于设计一种低成本，智能的安全和跟踪系统。