Computer Science Graduate Seminar
Monday, December 21, 2020, 1:15-2:15pm
Learning-based Visual Scene and Person Understanding for Mobile Robotics
- Alexander Hermans, M. Sc. – Chair for Computer Science 13
- Zoom: https://us02web.zoom.us/j/89686061674?pwd=UFUxaWMrdFJtY09JSWRBKzNxUnU0QT09
Meeting ID: 896 8606 1674
We have seen tremendous progress in the computer vision community across the past decades, especially with advancements in deep learning, which is now used as the core machine learning approach for most tasks. However, when deploying computer vision approaches in actual robotic applications, we often find that top-performing methods do not work as well as expected due to hardware constraints and different input data characteristics.
We deal with visual scene and person understanding which are highly relevant for robotics applications. Robots need to be able to understand their environment and take special care around persons to ensure a safe navigation and interaction. We specifically deal with three important sub-tasks: semantic segmentation, 2D laser-based object detection, and person re-identification. Semantic segmentation deals with the task of labeling every pixel or point in a scene with a class label. This can in turn be used to extract higher level information about the surrounding scene, which can be used as context for further planning and interaction tasks. While the resulting segmentations provide object labels, they do not contain instance labels, making it hard to detect object instances. However, object detection is an important capability for allowing robots to safely navigate between dynamic objects. Especially the detection of persons is an important task, enabling robots to interact with us. Since many mobile platforms are already equipped with a 2D laser scanner, they are interesting input sensors for object detection, even though the resulting scans only contain sparse data. In addition to person detection, person re-identification is an important task. This can be used to improve tracking, but also allows to gather longer-term statistics and enables person specific interactions.
For each of the three tasks we propose state-of-the-art approaches, however, we also consider aspects that are important for their real-world deployability and show applications of our methods within the context of robotics projects.
The computer science lecturers invite interested people to join.