A Deep Learning Approach to Drone Monitoring
This addresses drone detection and tracking for surveillance or security applications, but it appears incremental as it combines existing modules with data augmentation.
The paper tackles drone monitoring by developing an integrated detection and tracking system using deep learning, which performs well on real-world images with complex backgrounds even when trained on synthetic data.
A drone monitoring system that integrates deep-learning-based detection and tracking modules is proposed in this work. The biggest challenge in adopting deep learning methods for drone detection is the limited amount of training drone images. To address this issue, we develop a model-based drone augmentation technique that automatically generates drone images with a bounding box label on drone's location. To track a small flying drone, we utilize the residual information between consecutive image frames. Finally, we present an integrated detection and tracking system that outperforms the performance of each individual module containing detection or tracking only. The experiments show that, even being trained on synthetic data, the proposed system performs well on real world drone images with complex background. The USC drone detection and tracking dataset with user labeled bounding boxes is available to the public.