Abstract
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.
Highlights
U NDERWATER object tracking is pivotal in applications, such as underwater search and rescue operations, homeland and maritime security, deep ocean exploration, underwater robot navigation, and sea life monitoring [1]–[3]
Several object tracking methods have been proposed over the years [10]–[16], the bias of the publicly available benchmark datasets, which mostly focus on open-air visual data, has greatly skewed the strength of these tracking algorithms to open-air environments
This is because the visual data these trackers are trained and tested on are not representative of underwater scenarios. They each degrade in performance when tested on underwater scenarios as demonstrated in previous exploratory work [17]. This motivates the necessity to develop a comprehensive underwater database and benchmark to foster the development of tracking algorithms that will achieve comparatively high performance in both underwater and open-air environments
Summary
U NDERWATER object tracking is pivotal in applications, such as underwater search and rescue operations, homeland and maritime security, deep ocean exploration, underwater robot navigation, and sea life monitoring [1]–[3]. Several object tracking methods have been proposed over the years [10]–[16], the bias of the publicly available benchmark datasets, which mostly focus on open-air visual data, has greatly skewed the strength of these tracking algorithms to open-air environments. This is because the visual data these trackers are trained and tested on are not representative of underwater scenarios. 3) a new cascaded residual network for underwater image enhancement (CRN-UIE), a GAN-based enhancement method for improving the trackers’ performance on underwater data by translating visual data from the underwater domain to enhanced/clear underwater domain is presented.
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