水下
水准点(测量)
计算机科学
人工智能
计算机视觉
BitTorrent跟踪器
视频跟踪
图像质量
目标检测
对象(语法)
眼动
模式识别(心理学)
图像(数学)
地质学
海洋学
地理
大地测量学
作者
Karen Panetta,Landry Kezebou,Victor Oludare,Sos S. Agaian
出处
期刊:IEEE Journal of Oceanic Engineering
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:47 (1): 59-75
被引量:37
标识
DOI:10.1109/joe.2021.3086907
摘要
Current state-of-the-art object tracking methods have largely benefited from the public availability of numerous benchmark datasets. However, the focus has been on open-air imagery and much less on underwater visual data. Inherent underwater distortions, such as color loss, poor contrast, and underexposure, caused by attenuation of light, refraction, and scattering, greatly affect the visual quality of underwater data, and as such, existing open-air trackers perform less efficiently on such data. To help bridge this gap, this article proposes a first comprehensive underwater object tracking (UOT100) benchmark dataset to facilitate the development of tracking algorithms well-suited for underwater environments. The proposed dataset consists of 104 underwater video sequences and more than 74 000 annotated frames derived from both natural and artificial underwater videos, with great varieties of distortions. We benchmark the performance of 20 state-of-the-art object tracking algorithms and further introduce a cascaded residual network for underwater image enhancement model to improve tracking accuracy and success rate of trackers. Our experimental results demonstrate the shortcomings of existing tracking algorithms on underwater data and how our generative adversarial network (GAN)-based enhancement model can be used to improve tracking performance. We also evaluate the visual quality of our model's output against existing GAN-based methods using well-accepted quality metrics and demonstrate that our model yields better visual data.
科研通智能强力驱动
Strongly Powered by AbleSci AI