Abstract

For Augmented Reality to succeed in industrial appliances, industries demand not only robust and reliable tracking techniques, but also a scalable and performant pipeline, that is easy-to-integrate within the existing data- and content environment and that enables vendors to create tracking solutions on their own. In our demo, we present recent advances of our model tracking pipeline and tracking technology, which on the one hand is easy use and easy to integrate, while on the other hand robust enough during difficult environmental conditions and which delivers high accuracy for the industrial domain. We showcase our results inside an AR-manual scenario.

Abstract

Real-time object recognition on low-power embedded devices is a widely requested task, needed in manifold applications. However, it is still a demanding challenge to achieve desired performance goals. For example, for advanced driver assistance systems (ADAS) or autonomously driven cars, object recognition and lane detection are indispensable tasks. Another field of application is the continuous retrieval of the construction progress on-site for validation of the construction site status, by detecting installed components using a given CAD model. This paper presents a framework for highly customizable object detection systems implemented on a single heterogeneous computing chip leveraging FPGA logic and standard processors. The FPGA logic is used to implement a custom variation of the Hough Transform and further image processing tasks efficiently. The dedicated logic is supplemented with a software stack, which consists of a Linux operating system, including hardware access drivers, as well as high-level libraries like OpenCV and Robot Operating System (ROS) - all running on the same device. The capabilities of the system are demonstrated for three application scenarios, namely race track recognition, lane recognition and object detection tasks performed within a construction assistance system.

Abstract

To deal with the increasing demands in Air Traffic Control (ATC), new working place designs are proposed and developed that need novel human factors evaluation tools. In this paper, we propose a novel application of Electroencephalogram (EEG)-based emotion, workload, and stress recognition algorithms to investigate the optimal length of training for Air Traffic Control Officers (ATCOs) to learn working with threedimensional (3D) display as a supplementary to the existing 2D display. We tested and applied the state-of-the-art EEG-based subject-dependent algorithms. The following experiment was carried out. Twelve ATCOs were recruited to take part in the experiment. The participants were in charge of the Terminal Control Area, providing navigation assistance to aircraft departing and approaching the airport using 2D and 3D displays. EEG data were recorded, and traditional human factors questionnaires were given to the participants after 15-minute, 60- minute, and 120-minute training. Different from the questionnaires, the EEG-based evaluation tools allow the recognition of emotions, workload, and stress with different temporal resolutions during the task performance by subjects. The results showed that 50-minute training could be enough for the ATCOs to learn the new display setting as they had relatively low stress and workload. The study demonstrated that there is a potential of applying the EEG-based human factors evaluation tools to assess novel system designs in addition to traditional questionnaire and feedback, which can be beneficial for future improvements and developments of the systems and interfaces.