Object Detection in Equirectangular Panorama
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2018 24th International Conference on Pattern Recognition (ICPR) |
| Publisher | IEEE |
| Pages | 2190-2195 |
| Number of pages | 6 |
| ISBN (Electronic) | 978-1-5386-3788-3 |
| ISBN (Print) | 978-1-5386-3789-0 |
| DOIs | |
| Publication status | Published - Aug 2018 |
| Publication type | A4 Article in a conference publication |
| Event | International Conference on Pattern Recognition - Duration: 1 Jan 1900 → … |
Publication series
| Name | |
|---|---|
| ISSN (Print) | 1051-4651 |
Conference
| Conference | International Conference on Pattern Recognition |
|---|---|
| Period | 1/01/00 → … |
Abstract
We introduce a high-resolution equirectangular panorama (aka 360-degree, virtual reality, VR) dataset for object detection and propose a multi-projection variant of the YOLO detector. The main challenges with equirectangular panorama images are i) the lack of annotated training data, ii) high-resolution imagery and iii) severe geometric distortions of objects near the panorama projection poles. In this work, we solve the challenges by I) using training examples available in the “conventional datasets” (ImageNet and COCO), II) employing only low resolution images that require only moderate GPU computing power and memory, and III) our multi-projection YOLO handles projection distortions by making multiple stereographic sub-projections. In our experiments, YOLO outperforms the other state-of-the-art detector, Faster R-CNN, and our multi-projection YOLO achieves the best accuracy with low-resolution input.
Keywords
- image resolution, neural nets, object detection, virtual reality, YOLO detector, equirectangular panorama images, annotated training data, high-resolution imagery, panorama projection poles, training examples, conventional datasets, low resolution images, multiprojection YOLO, projection distortions, multiple stereographic sub-projections, low-resolution input, high-resolution equirectangular panorama dataset, geometric distortions, moderate GPU computing power, CNN, Detectors, Distortion, Object detection, Cameras, Image resolution, Virtual reality, Graphics processing units