Omnidirectional Pedestrian Detection by Rotation Invariant Training

Abstract

Recently much progress has been made in pedestrian detection by utilizing the learning ability of convolutional neural networks (CNNs). However, due to the lack of omnidirectional images to train CNNs, few CNN-based detectors have been proposed for omnidirectional pedestrian detection. One significant difference between omnidirectional images and perspective images is that the appearance of pedestrians is rotated in omnidirectional images. A previous method has dealt with this by transforming omnidirectional images into perspective images in the test phase. However, this method has significant drawbacks, namely, the computational cost and the performance degradation caused by the transformation. To address this issue, we propose a rotation invariant training method, which only uses randomly rotated perspective images without any additional annotation. By this method, existing large-scale datasets can be utilized. In test phase, omnidirectional images can be used without the transformation. To group predicted bounding boxes, we also develop a bounding box refinement, which works better for our detector than non-maximum suppression. The proposed detector achieved a state-of-the-art performance on four public benchmarks.