Fisheye8K_all_including_train&test_1.zip
- 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} }
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追加情報
フィールド | 値 |
---|---|
最終更新日 | unknown |
メタデータ最終更新日時 | 2023 / 6月 / 2, |
作成日 | 2023 / 6月 / 2, |
データ形式 | ZIP |
ライセンス | Creative Commons Attribution-NonCommercial 4.0 |
Media type | application/zip |
format | ZIP |
id | 9461c310-d2b2-45b6-afa7-0e2b2169f74c |
md5 | a9a2313737f54c13c122e5a9da11fdce |
package id | 003a91f5-ac12-47e6-8309-be46a8a55f7c |
position | 2 |
revision id | 6542dc14-5b32-4183-99f6-4b6161a31715 |
sha256 | 8ad25219ba7d0aebba20cbcbf2bfbbbfd4fb1ed8b0d3075c6c4834076ee108cb |
state | active |
作成日 | 1 年以上前 |
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