Data is key for advances in machine learning, including mobile applications like robots and autonomous cars. To ensure reliable operation, occurring scenarios must be reflected by the underlying dataset. Since the open-world environments can contain unknown scenarios and novel objects, active learning from online data collection and handling of unknowns is required. In this talk we discuss different approach to address this real world requirements.

Develop a YOLO-based object detection system specifically optimized for pedestrian detection in urban settings. The system will focus on real-time detection of pedestrians in different environments.