FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

FishEye8K

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080x1080 and 1280x1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640x640 and 1280x1280, respectively. The dataset will be available on the GitHub (https://github.com/MoyoG/FishEye8K) with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications.

@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} }

copyright

The FishEye8K dataset described on this page is distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which implies that you must: * (1) attribute the work as specified by the original authors, * (2) may not use this work for commercial purposes (for commercial use, please contact us), and * (3) if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. The dataset is provided "as it is" and we are not responsible for any subsequence from using this dataset.

Data and Resources

Additional Info

Field Value
Author
Version 2026.B1
Last Updated August 2, 2026, 17:28 (CST)
Created April 6, 2022, 14:52 (CST)
CVPR2023 Fisheye8K
FE-DETRAC
Fisheye8K
DOI 10.30193/scidm-ds-571593m

Citation


推薦資料集:


  • 商業登記家數及資本額異動-按縣市別分

    Payment instrument Free
    Update frequency Irregular
    商業登記家數及資本額異動,按縣市別分
  • 高雄市空氣品質人工測站監測數值2020年

    Payment instrument Free
    Update frequency Irregular
    Name(測站名稱)、Date(監測日期)、TSP(總懸浮微粒μg/m3)、PM10(懸浮微粒μg/m3)、Chloride(氯鹽μg/m3)、Nitrate(硝酸鹽μg/m3)、Sulfate(硫酸鹽μg/m3)、Lead(鉛μg/m3)、Dust(落塵量-公噸/平方公里/月)
  • 臺北市立動物園_館區簡介

    Payment instrument Free
    Update frequency Irregular
    本資料集提供臺北市立動物園園區所屬之館區簡介,包含戶外、室內及特展等三大類展示場域之館區簡介。(本資料集開放授權應用內容僅限文字範圍,圖片、聲音及影片等多媒體資訊不在開放授權範圍)
  • 科技部大屯火山觀測站_火山氣體監測

    Payment instrument Free
    Update frequency Irregular
    大屯火山觀測站挑選其中兩處具有代表性的溫泉進行每周一次的監測。這裡呈現的是溫泉中的溫度、總固容量(TDS)、酸鹼值(pH)、電導度等變化,而這些參數皆可以作為反映火山的活動性的指標之一。
  • 108年第3季花蓮縣印花稅徵收-1

    Payment instrument Free
    Update frequency Irregular
    108年第3季花蓮縣印花稅徵收