  We propose a segmentation-based bounding box generation method for
omnidirectional pedestrian detection, which enables detectors to tightly fit
bounding boxes to pedestrians without omnidirectional images for training.
Because the appearance of pedestrians in omnidirectional images may be rotated
to any angle, the performance of common pedestrian detectors is likely to be
substantially degraded. Existing methods mitigate this issue by transforming
images during inference or training detectors with omnidirectional images.
However, the first approach substantially degrades the inference speed, and the
second approach requires laborious annotations. To overcome these drawbacks, we
leverage an existing large-scale dataset, whose segmentation annotations can be
utilized, to generate tightly fitted bounding box annotations. We also develop
a pseudo-fisheye distortion augmentation method, which further enhances the
performance. Extensive analysis shows that our detector successfully fits
bounding boxes to pedestrians and demonstrates substantial performance
improvement.
