Fisheye8K_all_including_train&test_1.zip

URL: https://scidm.nchc.org.tw/en/dataset/fe-detrac/resource/9461c310-d2b2-45b6-afa7-0e2b2169f74c/nchcproxy

  • this the all dataset files including train/ and test/ folders.

Description

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{Munk_2023_CVPR_Workshops, author = {Munkhjargal Gochoo and Munkh-Erdene Otgonbold and Erkhembayar Ganbold and Hsieh, Jun-Wei and Chang, Ming-Ching and Chen, Ping-Yang and Byambaa Dorj and Hamad Al Jassmi and Ganzorig Batnasan and Fady Alnajjar and Mohammed Abduljabbar 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} }

There are no views created for this resource yet.

Additional Information

Field Value
Data last updated June 2, 2023
Metadata last updated June 2, 2023
Created June 2, 2023
Format application/zip
License Creative Commons Attribution-NonCommercial 4.0
createdover 3 years ago
doi10.30193/scidm-rs-p15801k
doi date published2023-06-02
formatZIP
id9461c310-d2b2-45b6-afa7-0e2b2169f74c
md5c93f2c08dc057c04e2fda60abb1ca31d
mimetypeapplication/zip
package id003a91f5-ac12-47e6-8309-be46a8a55f7c
position2
revision id78dd5b39-59a8-4323-ac0d-ef395b335ad3
sha2565c4fc794e3829d872d85a64809938eb2e521c0166707274e952d99194c55463e
stateactive

推薦資料集:


  • 經濟部能源局_能源研究發展基金對民間團體及個人補捐助案件彙總表

    Payment instrument Free
    Update frequency Irregular
    提供能源研究發展基金對補助民間團體及個人補捐助案件明細資料
  • 債券持有對象分析

    Payment instrument Free
    Update frequency Irregular
    債券持有對象依個人戶、法人戶、自營商、保管機構及其他之分類持有比率分析資訊(臺灣集中保管結算所)
  • 環境影響說明書審查結論

    Payment instrument Free
    Update frequency Irregular
    環境影響說明書審查結論。(資料來源網址:https://eiadoc.epa.gov.tw/eiaweb/)
  • 2016年版縣市社會脆弱度指數

    Payment instrument Free
    Update frequency Irregular
    社會脆弱度綜合指數依據暴露量、減災整備、應變能力、復原能力等四大方向進行指標選取,共計採用31項指標,評估全台22縣市之社會脆弱度情況。Z指數指針對不同地區間社會脆弱度情況進行比較之結果。
  • 役男徵兵檢查及身心障礙與重大傷病書面審核人數統計表

    Payment instrument Free
    Update frequency Irregular
    縣市政府辦理徵兵檢查前,向社政單位比對役男身心障礙資料,或可由役男檢具效期內身心障礙或重大傷病證明,凡符合「身心障礙或重大傷病與體位區分標準免役體位判等對照表」規定者,得由徵兵檢查會逕判免役體位,免再行徵兵體檢,以簡政又便民