An image and video dataset of nesting green sea turtles with annotated data

Photo- and video-based reidentification of green sea turtles using their natural markers is far less invasive than artificial tagging. An RGB camera mounted on a man-portable rig, was used to collect video data on Greater Talang Island (1 °54’45′′N 109 °46’33′′E) from September to October 2022, and September 2023. This islet is located 30 minutes offshore from the Sematan district in Southwest Sarawak, Malaysia. In total, 42 videos of 42 unique individuals were captured, from which 14,471 video frames were extracted and annotated with a bounding box each. Through manual inspection, the annotation data were diligently checked and edited if necessary. Additionally, 130 selected frames were further annotated each with a region-of-interest mask containing only the carapace. These data have the potential to aid researchers in training computer vision models for various tasks such as counting, segmentation and individual reidentification.

Publication Details

Journal
Unimas Institutional Repository (Universiti Malaysia Sarawak)
Published
2024-12-27
Primary Topic
Magnetic confinement fusion research
Type
article
Field-Weighted Citation Impact
0.00

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article

An image and video dataset of nesting green sea turtles with annotated data

Unimas Institutional Repository (Universiti Malaysia Sarawak)
Magnetic confinement fusion research
article

An image and video dataset of nesting green sea turtles with annotated data

article en

Abstract

Photo- and video-based reidentification of green sea turtles using their natural markers is far less invasive than artificial tagging. An RGB camera mounted on a man-portable rig, was used to collect video data on Greater Talang Island (1 °54’45′′N 109 °46’33′′E) from September to October 2022, and September 2023. This islet is located 30 minutes offshore from the Sematan district in Southwest Sarawak, Malaysia. In total, 42 videos of 42 unique individuals were captured, from which 14,471 video frames were extracted and annotated with a bounding box each. Through manual inspection, the annotation data were diligently checked and edited if necessary. Additionally, 130 selected frames were further annotated each with a region-of-interest mask containing only the carapace. These data have the potential to aid researchers in training computer vision models for various tasks such as counting, segmentation and individual reidentification.

Unimas Institutional Repository (Universiti Malaysia Sarawak)
Universiti Malaysia Sarawak
Life below water
Openalex Percentile: Top 49%
Magnetic confinement fusion research
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