The design of automated video surveillance systems often involves the detection of agents which exhibit anomalous or dangerous behavior in the scene under analysis. Models aimed to enhance the video pattern recognition abilities of the system are commonly integrated in order to increase its performance. Deep learning neural networks are found among the most popular models employed for this purpose. Nevertheless, the large computational demands of deep networks mean that exhaustive scans of the full video frame make the system perform rather poorly in terms of execution speed when implemented on low cost devices, due to the excessive computational load generated by the examination of multiple image windows. This work presents a video surveillance system aimed to detect moving objects with abnormal behavior for a panoramic 360∘ surveillance camera. The block of the video frame to be analyzed is determined on the basis of a probabilistic mixture distribution comprised by two mixture components. The first component is a uniform distribution, which is in charge of a blind window selection, while the second component is a mixture of kernel distributions. The kernel distributions generate windows within the video frame in the vicinity of the areas where anomalies were previously found. This contributes to obtain candidate windows for analysis which are close to the most relevant regions of the video frame, according to the past recorded activity. A Raspberry Pi microcontroller based board is employed to implement the system. This enables the design and implementation of a system with a low cost, which is nevertheless capable of performing the video analysis with a high video frame processing rate.

中文翻译：

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由低成本硬件和全景相机管理的基于深度学习的视频监控系统

自动化视频监视系统的设计通常涉及检测要分析的场景中出现异常或危险行为的代理。为了提高系统性能，通常将旨在增强系统视频模式识别能力的模型集成在一起。深度学习神经网络被发现用于此目的的最流行模型中。然而，深层网络的大量计算需求意味着，由于要检查多个图像窗口而产生的过大计算量，因此在低成本设备上实施时，对完整视频帧进行详尽的扫描会使系统在执行速度方面表现较差。这项工作提出了一种视频监视系统，旨在为全景360°监视摄像机检测具有异常行为的运动物体。根据由两个混合分量组成的概率混合分布确定要分析的视频帧的块。第一个成分是均匀分布，负责盲窗选择，而第二个成分是内核分布的混合。内核分布会在视频帧内的先前发现异常区域附近生成窗口。根据过去记录的活动，这有助于获得接近视频帧最相关区域的待分析候选窗口。使用基于Raspberry Pi微控制器的板来实现系统。这样可以低成本设计和实施系统，