In this project, we explore the design and development of a class of robust diver-following algorithms for autonomous underwater robots. By considering the operational challenges for underwater visual tracking in diverse real-world settings, we formulate a set of desired features of a generic diver following algorithm. We attempt to accommodate these features and maximize general tracking performance by exploiting the SOTA deep object detection models: Faster R-CNN (Inception V2), YOLO V2, Tiny YOLO, and SSD (MobileNet V2).
Figure: Schematic diagrams of the proposed CNN-based model
Subsequently, we design an architecturally simple CNN-based diver-detection model that is much faster than the SOTA deep models yet provides comparable detection performance. Each building block of the proposed model was fine-tuned in order to balance the trade-off between robustness and efficiency for a single-board setting under real-time constraints. We also validated its tracking performance and general applicability through numerous field experiments in pools and oceans. This diver-following module is currently used by our Aqua MinneBot AUV during field experiments.