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Deep learning-based video surveillance system managed by low cost hardware and panoramic cameras

机译：基于深度学习的视频监控系统由低成本硬件和全景摄像机管理

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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 degrees 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.
机译：自动视频监控系统的设计往往涉及检测在分析下在现场表现出异常或危险行为的药剂。旨在增强系统的视频模式识别能力的模型通常集成，以提高其性能。深度学习神经网络是为此目的而受雇的最受欢迎的模型。尽管如此，深网络的大型计算需求意味着，由于通过检查多个图像窗口产生的过度计算负载，在低成本设备上实现时，整个视频帧的详尽扫描使系统在执行速度方面执行相当差。 。这项工作提出了一种视频监控系统，旨在检测全景360度监视摄像机的异常行为的移动物体。待分析的视频帧的块是基于由两个混合物组分构成的概率混合分布来确定的。第一组件是均匀分布，其负责盲目窗口选择，而第二组分是核分布的混合。内核发行版在先前发现异常的区域附近生成窗口。根据过去的录制活动，这有助于获取用于分析的候选窗口，该分析靠近视频帧的最相关区域。基于覆盆子PI微控制器的板用于实现系统。这使得具有低成本的系统的设计和实现，这仍然能够以高视频帧处理速率执行视频分析。

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