FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection
Munkhjargal Gochoo, Munkh-Erdene Otgonbold, Erkhembayar Ganbold,, Jun-Wei Hsieh, Ming-Ching Chang, Ping-Yang Chen, Byambaa Dorj, Hamad Al, Jassmi, Ganzorig Batnasan, Fady Alnajjar, Mohammed Abduljabbar, Fang-Pang Lin

TL;DR
This paper introduces FishEye8K, a comprehensive fisheye camera dataset for road object detection, along with benchmark results of state-of-the-art models, addressing a gap in traffic surveillance data for fisheye lenses.
Contribution
It provides the first large-scale open dataset for fisheye camera traffic monitoring, including annotations and benchmark results for multiple models.
Findings
YOLOv8 and YOLOR outperform other models on the dataset.
The dataset contains 157K bounding boxes across five classes.
The dataset is available in multiple annotation formats on GitHub.
Abstract
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 10801080 and 12801280. The data annotation and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Neural Network Applications · Video Surveillance and Tracking Methods · Autonomous Vehicle Technology and Safety
MethodsYou Only Look Once · Test
