Adaptive Visual Face Tracking for an Autonomous Robot

Perception is an essential ability for autonomous robots in non-standardized conditions. However, the appearance of objects can change between different conditions. A system visually tracking a target based on its appearance could lose its target in those cases. A tracker learning the appearance of the target in different conditions should perform better at this task. To learn reliably, the system needs feedback. In this study, feedback is provided by a secondary teacher system that trains the tracker. The learning tracker is compared to a baseline non-learning tracker using data from an autonomous mobile robot operating in realistic conditions. In this experiment, the learning tracker outperforms the non-learning tracker. This shows that we successfully used the teacher system to train a visual tracking system on an autonomous robot.