Datasets:

Voxel51
/

fisheye8k

@@ -58,13 +58,7 @@ dataset_summary: '
   '
 ---
-# Dataset Card for fisheye8k
-<!-- Provide a quick summary of the dataset. -->
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 8000 samples.
@@ -93,132 +87,82 @@ session = fo.launch_app(dataset)
 ## Dataset Details
-### Dataset Description
-<!-- Provide a longer summary of what this dataset is. -->
-- **Curated by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Language(s) (NLP):** en
-- **License:** [More Information Needed]
-### Dataset Sources [optional]
-<!-- Provide the basic links for the dataset. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 ## Uses
-<!-- Address questions around how the dataset is intended to be used. -->
-### Direct Use
-<!-- This section describes suitable use cases for the dataset. -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
-[More Information Needed]
 ## Dataset Structure
-<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
-[More Information Needed]
 ## Dataset Creation
-### Curation Rationale
-<!-- Motivation for the creation of this dataset. -->
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 ### Source Data
-<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
-#### Data Collection and Processing
-<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
-[More Information Needed]
-#### Who are the source data producers?
-<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
-[More Information Needed]
-### Annotations [optional]
-<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
-#### Annotation process
-<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
-[More Information Needed]
-#### Who are the annotators?
-<!-- This section describes the people or systems who created the annotations. -->
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-#### Personal and Sensitive Information
-<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
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-## Glossary [optional]
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   '
 ---
+# Dataset Card for FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection
 This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 8000 samples.
 ## Dataset Details
+Fisheye lens provides omnidirectional, wide coverage for using fewer cameras to monitor road intersections, however, with view distortions.
+The FishEye8K benchmark dataset for road object detection tasks comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck).
+The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080×1080 and 1280×1280.
+- **Curated by:** Munkhjargal Gochoo, et. al.
+- **Shared by:** [Harpreet Sahota](https://huggingface.co/harpreetsahota)
+- **Language(s) (NLP):** en
+- **License:** Creative Commons Attribution-NonCommercial-ShareAlike 4.0
+### Dataset Sources
+- **Repository:** https://github.com/MoyoG/FishEye8K
+- **Website:** https://scidm.nchc.org.tw/en/dataset/fisheye8k
+- **Paper:** https://arxiv.org/abs/2305.17449
 ## Uses
+Object detection
 ## Dataset Structure
+  The filename is expected to follow the format: cameraX_T_YYY.png, where:
+  - X is the camera number.
+  - T is the time of day indicator (A for afternoon, E for evening, N for night, M for morning).
+  - YYY is an arbitrary sequence of digits.
 ## Dataset Creation
 ### Source Data
+The authors acquired 35 fisheye videos captured using 20 traffic surveillance cameras at 60 FPS in Hsinchu City, Taiwan. Among them, the first set of 30 videos (Set 1) was recorded by the cameras mounted at Nanching Hwy Road on July 17, 2018, with 1920 × 1080 resolution, and
+each video lasts about 50-60 minutes.
+The second set of 5 videos (Set 2) was recorded at 1920 × 1920 resolution; each video lasts about 20 minutes.
+All cameras are the property of the local police department, so there is no user consent or license issue.
+All images in the dataset are made available to the public for academic and R&D use.
+### Annotation Rule
+The road participants were annotated based on their clarity and recognizability to the annotators,
+regardless of their location. In some cases, distant objects were also annotated based on this criterion.
+### Annotation
+Two researchers/annotators manually labelled over 10,000 frames using the DarkLabel annotation program over one year.
+After cleaning the dataset, 8,000 frames containing 157012 bounding boxes remained.
+Unsuitable frames were removed, including those featuring road participants outside the five classes of interest
+# Data Anonymization
+The identification of road participants, such as people’s faces and vehicle license plates from the dataset images was found to be unfeasible due for various reasons. The cameras used for capturing the images were installed at a higher
+ground level, making capturing clear facial features or license plates difficult, especially when they are far away.
+Additionally, the pedestrians are not looking at the cameras, and license plates appear too small when viewed from a distance.
+However, to maintain ethical compliance and protect the privacy of the road participants, we blurred the areas of the images containing the faces of pedestrians and the
+license plates of vehicles whenever they were visible.
+## Citation
+```bibtex
+@InProceedings{Gochoo_2023_CVPR,
+    author    = {Gochoo, Munkhjargal and Otgonbold, Munkh-Erdene and Ganbold, Erkhembayar and Hsieh, Jun-Wei and Chang, Ming-Ching and Chen, Ping-Yang and Dorj, Byambaa and Al Jassmi, Hamad and Batnasan, Ganzorig and Alnajjar, Fady and Abduljabbar, Mohammed and Lin, Fang-Pang},
+    title     = {FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
+    month     = {June},
+    year      = {2023},
+    pages     = {5304-5312}
+}
+```