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ATM Protection Using Embedded Deep Learning Solutions

机译：使用嵌入式深度学习解决方案的ATM保护

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Last decade advances in Deep Learning methods lead to sensible improvements in state of the art results in many real world applications, thanks to the exploitation of particular Artificial Neural Networks architectures. In this paper we present an investigation of the application of such kind of structures to a Video Surveillance case of study, in which the special nature and the small amount of available data increases the difficulties during the training phase. The analyzed scenario involves the protection of Automatic Teller Machines (ATM), representing a sensitive problem in the world of both banking and public security. Because of the critical issues related to this environment, even apparently small improvements in either accuracy or responsiveness of surveillance systems can produce a fundamental contribution. Even if the experimentation has been reproduced in an artificial scenario, the results show that the implemented architecture is able to classify depth data in real-time on an embedded system, detecting all the test attacks in a few seconds.
机译：过去十年来，由于采用了特定的人工神经网络体系结构，深度学习方法的进步导致了许多实际应用中的最先进结果的合理改进。在本文中，我们对这种结构在视频监控案例中的应用进行了调查，在这种案例中，特殊性质和少量可用数据增加了培训阶段的难度。分析的场景涉及自动提款机（ATM）的保护，这代表了银行和公共安全领域中的一个敏感问题。由于与该环境有关的关键问题，即使监视系统的准确性或响应性即使很小的改善也可以产生根本性的贡献。即使实验是在人工场景下进行的，结果也表明，所实现的体系结构能够在嵌入式系统上实时对深度数据进行分类，从而在几秒钟内检测到所有测试攻击。

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