Underwater Object Tracking Benchmark and Dataset

Kezebou, Landry; Oludare, Victor; Panetta, Karen; Agaian, Sos S.

doi:10.1109/hst47167.2019.9032954

Cited by 15 publications

(10 citation statements)

References 21 publications

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“…On the other hand, object tracking can locate and output the movement direction and speed of objects between video frames. In marine ecosystems, object tracking has been used to track on‐surface objects (see topios.org) and underwater objects such as fish, sea turtles, dolphins, and whales (Arvind et al., 2019; Chuang et al., 2017; Kezebou et al., 2019; Spampinato et al., 2008; Xu & Cheng, 2017).…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of fish and tracking of movement for ecology

Lopez‐Marcano

Jinks

Buelow

et al. 2021

Ecology and Evolution

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Section: Introductionmentioning

confidence: 99%

Automatic detection of fish and tracking of movement for ecology

Lopez‐Marcano

Jinks

Buelow

et al. 2021

Ecology and Evolution

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“…1, with each column showing variations of similar types of visual data. The UOT100 benchmark dataset is a substantially improved version over an earlier dataset, i.e., UOT32 [17]. UOT100 is a much larger dataset and includes many more underwater distortion categories.…”

Section: A Uot100 Datasetmentioning

confidence: 99%

“…This is because the visual data these trackers are trained and tested on are not representative of underwater scenarios. As such, 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.…”

Section: Introductionmentioning

confidence: 97%

Comprehensive Underwater Object Tracking Benchmark Dataset and Underwater Image Enhancement With GAN

Panetta

Kezebou

Oludare

et al. 2022

IEEE J. Oceanic Eng.

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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. Index Terms-Underwater benchmark dataset, underwater generative adversarial network (GAN), underwater image enhancement (UIE), underwater object tracking (UOT).

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“…Tracking) Dataset [47] The benchmark dataset for underwater tracking has 32 videos with a total of 24,241 annotated frames and an average duration of 29.15 seconds and frame count of 757.53. sequences for objects of interest.…”

Section: Uot32 (Underwater Objectmentioning

confidence: 99%

Review on Deep Learning Techniques for Underwater Object Detection

Dakhil¹,

Khayeat²

2022

Data Science and Machine Learning

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Repair and maintenance of underwater structures as well as marine science rely heavily on the results of underwater object detection, which is a crucial part of the image processing workflow. Although many computer vision-based approaches have been presented, no one has yet developed a system that reliably and accurately detects and categorizes objects and animals found in the deep sea. This is largely due to obstacles that scatter and absorb light in an underwater setting. With the introduction of deep learning, scientists have been able to address a wide range of issues, including safeguarding the marine ecosystem, saving lives in an emergency, preventing underwater disasters, and detecting, spooring, and identifying underwater targets. However, the benefits and drawbacks of these deep learning systems remain unknown. Therefore, the purpose of this article is to provide an overview of the dataset that has been utilized in underwater object detection and to present a discussion of the advantages and disadvantages of the algorithms employed for this purpose.

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Underwater Object Tracking Benchmark and Dataset

Cited by 15 publications

References 21 publications

Automatic detection of fish and tracking of movement for ecology

Automatic detection of fish and tracking of movement for ecology

Comprehensive Underwater Object Tracking Benchmark Dataset and Underwater Image Enhancement With GAN

Review on Deep Learning Techniques for Underwater Object Detection

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