Abstract
360° camera has recently become popular since it can capture the whole 360° scene. A large number of related applications have been springing up. In this paper, We propose a deep learning based object detector that can be applied directly on 360° images. The proposed detector is based on modifications of the faster RCNN model. Three modification schemes are proposed here, including (1) distortion data augmentation, (2) introducing muilti-kernel layers for improving accuracy for distorted object detection, and (3) adding position information into the model for learning spatial information. Additionally, we create two datasets, 360GoogleStreetView and 360Videos, and perform experiments on these two datasets to demonstrate that our object detector provides superior accuracy for object detection directly on 360° images.