ATM Protection Using Embedded Deep Learning Solutions

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.