Abstract
Nowadays, Ocean Observatory Networks, Which Gather and Provide Multidisciplinary, Long-Term, 3d Continuous Marine Observations at Multiple Temporal Spatial Scales, Play a More and More Important Role in Ocean Investigations. in This Paper, We First Perform Image Enhancement to Produce Depth Information and Benefit Many Vision Algorithms and Advanced Image Editing. We Try to Develop a Novel Underwater Fish Detection and Tracking Strategies Combining You Only Look Once (Yolo) Latest Detection Algorithm Yolov3 Algorithm and Parallel Correlation Filter. We Demonstrated on the Nvidia Jetson Tx2 for Online Fish Detection and Tracking, Enabling a Fast System and Rapid Experimentation. It Has Been Shown in the Experiments that the Developed Scheme of This Paper Achieves Consistent Performance Improvements on Online Fish Detection and Tracking for Ocean Observatory Network.
Recommended Citation
S. Liu et al., "Embedded Online Fish Detection and Tracking System Via Yolov3 and Parallel Correlation Filter," OCEANS 2018 MTS/IEEE Charleston, OCEAN 2018, article no. 8604658, Institute of Electrical and Electronics Engineers, Jan 2019.
The definitive version is available at https://doi.org/10.1109/OCEANS.2018.8604658
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Detection algorithm; Fish detection and tracking; Ocean Observatory Network; Parallel Correlation Filter
International Standard Book Number (ISBN)
978-153864814-8
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
07 Jan 2019
