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The purpose of this study is to explore the possibility of using selected imaging technologies in automated video surveillance systems. The main goal of this project is to handle events that may lead to security risks, injuries, etc in various environments without relaying on more conventional sensors such as infrared photocells. For this purpose it is necessary to perform a thorough analysis of the events to be interpreted as situations of interest. It is also important to consider the hardware requirements and restrictions for developing such system. The project requires defining a hardware as well as software platform(s) and their integration into an automated tool. This paper describes the implementation of the famous Microsoft Kinect 2.0 depth sensor (well known in gaming and recreational applications) for shape/skeleton detection, and its integration into an artificial intelligence based platform utilizing selected machine learning methods. The author reveals the system implementation details, and then demonstrates its shape detection capabilities while in operation.

Hybrid imaging-AI approach for handling critical situations in a fast-changing environment: preliminary study