Abstract

An effective (performance- and accuracy-wise) computational method for pattern recognition in a continuous video stream using deep neural networks for access control systems is proposed. The class of recognition problems solved by the method using a sequence of video stream frames is identified: the vehicle itself and the characters on its license plate (LP), faces of people, and abnormal situations. In contrast to the known solutions, a classification with a subsequent reinforcement based on multiple frames of a video stream and with an algorithm for the automatic annotation of images is used. Neural network architectures with independent recurrent layers for classifying video fragments adapted for the problems, a dual network for face recognition, and a deep neural network for vehicle character recognition are proposed. New databases for neural network training are created. A schematic diagram of an intelligent access control system for ensuring the security of an enterprise, a distinctive feature of which is the use of a multirotor unmanned aerial vehicle with a computing unit, is proposed. Field experiments are carried out, and the accuracy and performance of the computational method in solving each problem are assessed. Software modules in the Python language for solving tasks of the intelligent access control system are developed.

中文翻译：

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使用访问控制系统的深度神经网络识别连续视频流帧中的状况和对象的计算方法

摘要

提出了一种有效的（性能和准确度）计算方法，该方法用于使用访问控制系统的深度神经网络在连续视频流中进行模式识别。通过使用一系列视频流帧的方法解决的识别问题类别将被识别：车辆本身及其车牌（LP）上的字符，人脸和异常情况。与已知解决方案相反，使用了基于视频流的多个帧的具有后续增强的分类以及具有用于图像的自动注释的算法。提出了一种具有独立循环层的神经网络体系结构，用于对适应于该问题的视频片段进行分类，用于人脸识别的双重网络和用于车辆字符识别的深度神经网络。创建了用于神经网络训练的新数据库。提出了一种用于确保企业安全的智能门禁系统的示意图，该系统的显着特征是使用具有计算单元的多旋翼无人机。进行了现场实验，并评估了计算方法解决每个问题的准确性和性能。开发了用于解决智​​能访问控制系统任务的Python语言软件模块。并评估了计算方法解决每个问题的准确性和性能。开发了用于解决智​​能访问控制系统任务的Python语言软件模块。并评估了计算方法解决每个问题的准确性和性能。开发了用于解决智​​能访问控制系统任务的Python语言软件模块。