Long-Term Person Tracking for Unmanned Aerial Vehicle Based on Human-Machine Collaboration
Tongtong Zhou, Yadong Liu
Abstract
Unmanned Aerial Vehicle (UAV) has been widely used in military reconnaissance, smart transportation, public security and other fields. UAV-based person tracking is attracting incremental attention for its wide application requirements. Currently, some state-of-the-art visual tracking methods have achieved promising performance in common scenarios. However, in the scene of UAV-based person tracking, there will be long-term target disappearance and unpredictable dramatic target appearance changes, which still pose a huge challenge to UAV-based person tracking. In this work, a human-machine hybrid augmented tracking system based on eye tracking is proposed to cope with the challenge. During tracking, through the interaction between humans and machines, humans can provide real-time guidance and corrections to the tracker, and the tracker can also learn interesting targets from humans to enhance itself. The experimental results show that human-in-the-loop can remarkable improve the success rate and robustness of the tracking and our tracking system outperforms the state-of-the-art tracker in complex environments.