Tracking the Fine Scale Movements of Fish using Autonomous Maritime Robotics: A Systematic State of the Art Review

This paper provides a systematic state of the art review on tracking the fine scale movements of fish with the use of autonomous maritime robotics. Knowledge of migration patterns and the localization of specific species of fish at a given time is vital to many aspects of conservation. This paper reviews these technologies and provides insight into what systems are being used and why. The review results show that a larger amount of complex systems that use a deep learning techniques are used over more simplistic approaches to the design. Most results found in the study involve Autonomous Underwater Vehicles, which generally require the most complex array of sensors. The results also provide insight into future research such as methods involving swarm intelligence, which has seen an increase in use in recent years. This synthesis of current and future research will be helpful to research teams working to create an autonomous vehicle with intentions to track, navigate or survey

Novel event analysis for human-machine collaborative underwater exploration

One of the main task for deep sea submersible is for human-machine collaborative scientific exploration, e.g., human ourselves drive the submersible and monitor cameras around the submersible to observe new species fish or strange topography in a tedious way. In this paper, by defining novel marine animals or any extreme events as novel events, we design a new deep sea novel visual event analysis framework to improve the efficiency of human-machine collaboration and improve the accuracy simultaneously. Specifically, our visual framework concerns diverse functions than most state-of-the-arts, including novel event detection, tracking and summarization. Due to the power and computation resource limitation of the submersible, we design an efficient deep learning based visual saliency method for novel event detection and propose an online object tracking strategy as well. All the experiments are depending on Chinese Jiaolong, the manned deep sea submersible, which mounts several PanCtiltCzoom (PTZ) camera and static cameras. We build a new novel deep sea event dataset and the results justify that our human-machine collaborative visual observation framework can automatically detect, track and summarize the novel deep sea event. (C) 2019 Elsevier Ltd. All rights reserved