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Manual annotation for human action recognition with content semantics using 3D Point Cloud (3D-PC) in industrial environments consumes a lot of time and resources. This work aims to recognize, analyze, and model human actions to develop a framework for automatically extracting content semantics. Main Contributions of this work: 1. design a multi-layer structure of various DNN classifiers to detect and extract humans and dynamic objects using 3D-PC preciously, 2. empirical experiments with over 10 subjects for collecting datasets of human actions and activities in one industrial setting, 3. development of an intuitive GUI to verify human actions and its interaction activities with the environment, 4. design and implement a methodology for automatic sequence matching of human actions in 3D-PC. All these procedures are merged in the proposed framework and evaluated in one industrial Use-Case with flexible patch sizes. Comparing the new approach with standard methods has shown that the annotation process can be accelerated by 5.2 times through automation.

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Das Softwaretool „SafeZone“ erlaubt die effiziente und zuverlässige Berechnung von Sicherheitsbereichen (Safe Bereichen) für komplexe schutzzaunlose Roboteranlagen, zum Beispiel im Karosseriebau und der Endmontage. Unter Berücksichtigung der realen Bewegungsbahnen und Geschwindigkeiten werden – im Gegensatz zum Status Quo – nur dort Gefahrenbereiche berechnet, wo auch Gefahren entstehen können. Das spart Platz und lässt Mensch sowie Roboter sicher näher zusammenrücken.   The „SafeZone“ software tool enables the efficient and reliable calculation of safety areas (safe zones) for complex robot systems without protective fences, such as car body construction and final assembly. Considering real motion paths and speeds, safe zones are calculated – in contrast to the status quo – only where hazards can occur. This saves space and allows humans and robots to move closer together safely.