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Object Detection in Equirectangular Panorama

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition (ICPR)
Number of pages6
ISBN (Electronic)978-1-5386-3788-3
ISBN (Print)978-1-5386-3789-0
Publication statusPublished - Aug 2018
Publication typeA4 Article in a conference publication
EventInternational Conference on Pattern Recognition -
Duration: 1 Jan 1900 → …

Publication series

ISSN (Print)1051-4651


ConferenceInternational Conference on Pattern Recognition
Period1/01/00 → …


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.


  • 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

Publication forum classification

Field of science, Statistics Finland