In our lab, … some general text
Adversarial Machine Learning
Deep learning models have become a common feature in industry as well as everyday life. They are used to make decisions in autonomic driving, healthcare, security, and more. However, it has been shown that these models are vulnerable to small adversarial perturbations to their input, which can completely change their prediction. This limits the deployment of deep learning models in industrial applications. Our research concerns the analysis of these vulnerabilities and approaches for avoiding them.
Deep Learning for Online Handwriting Recognition
Different deep learning models have been developed for OHWR. They are used in daily life applications in mobile phones and different hand help devices. Yet, the developed models rely on specific writing tools and writing surfaces. We develop a system for OHWR using a regular ballpoint-pen equipped with several IMU sensors. Our methods rely on the data transmitted by the sensors, and allow OHWR in real time while writing on regular paper, with no use to a specific writing surface.
Deep Learning for Localization in Lidar Environments
Lidar images contain precise measurements for positions of objects, compared to RGB images. However, object detection and recognition on Lidar images are still mostly driven by conventional algorithms, compared to the current state-of-the-art in image processing: Deep Learning. One problem is the lower quantity of Lidar images, the other is the defintion and computation of locality. Our methods rely on a mixture of conventional and deep learning models to locate objects in a Lidar scene e.g. Twistlocks in Terminal Environments. Conventional models are applied to ensure robust behaviour, whereas Deep Learning models are only used to estimate a probability of object locations. Combining both methods ensures a robust and precise system for operation in terminal environment.
For many applications in various fields it is important to analyse images or videos. With the help of machine learning approaches it is possible to localise and classify objects appearing in images. Deep convolutional networks (e.g. YOLO) provide an end-to-end object detection. A very popular and widely-used application of object detection is face detection.
In our lab we use object recognition mainly in the research areas of industry (?) and sports analytics.
Deep learning-based event detection in team sports
In team sports, the coach as well as individual athletes are heavily interested in automated recognition and analysis of on-pitch sport specific activities. In recent years, inertial measurement units in combination with machine learning methods are explored as alternative to camera-based systems. Still, the analysis of complex movement patterns remains challenging. As the amount of available data is increasing rapidly, novel deep learning methods showing promising results in related fields are applied to extend existing methods.
Clinical decision support systems
Clinical decisions may be based on subjective ratings and patient reported outcome measures which do not necessarily fully capture functional limitations, before or after an intervention. We aim to develop tools to complement those ratings by objective tools and by identifying responders / non-responders in order to provide the best treatment for the individual patient.