Abstract
This letter explores the design and development of a class of robust diver detection algorithms for autonomous diver-following applications. 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 state-of-the-art deep object detection models. We fine tune the building blocks of these models with a goal of balancing the tradeoff between robustness and efficiency in an on-board setting under real-time constraints. Subsequently, we design an architecturally simple convolutional neural network based diver detection model that is much faster than the state-of-the-art deep models yet provides comparable detection performances. In addition, we validate the performance and effectiveness of the proposed model through a number of diver-following experiments in closed-water and open-water environments.
Original language | English (US) |
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Article number | 8543168 |
Pages (from-to) | 113-120 |
Number of pages | 8 |
Journal | IEEE Robotics and Automation Letters |
Volume | 4 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2019 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Keywords
- Human detection and tracking
- field robots
- marine robotics