  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 1080$\times$1080 and 1280$\times$1280. The data annotation and
validation process were arduous and time-consuming, due to the ultra-wide
panoramic and hemispherical fisheye camera images with large distortion and
numerous road participants, particularly people riding scooters. To avoid bias,
frames from a particular camera were assigned to either the training or test
sets, maintaining a ratio of about 70:30 for both the number of images and
bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR
outperform on input sizes 640$\times$640 and 1280$\times$1280, respectively.
The dataset will be available on GitHub 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.
