  We propose a segmentation-based bounding box generation method for
omnidirectional pedestrian detection that enables detectors to tightly fit
bounding boxes to pedestrians without omnidirectional images for training. Due
to the wide angle of view, omnidirectional cameras are more cost-effective than
standard cameras and hence suitable for large-scale monitoring. The problem of
using omnidirectional cameras for pedestrian detection is that the performance
of standard pedestrian detectors is likely to be substantially degraded because
pedestrians' appearance in omnidirectional images may be rotated to any angle.
Existing methods mitigate this issue by transforming images during inference.
However, the transformation substantially degrades the detection accuracy and
speed. A recently proposed method obviates the transformation by training
detectors with omnidirectional images, which instead incurs huge annotation
costs. To obviate both the transformation and annotation works, we leverage an
existing large-scale object detection dataset. We train a detector with rotated
images and tightly fitted bounding box annotations generated from the
segmentation annotations in the dataset, resulting in detecting pedestrians in
omnidirectional images with tightly fitted bounding boxes. We also develop
pseudo-fisheye distortion augmentation, which further enhances the performance.
Extensive analysis shows that our detector successfully fits bounding boxes to
pedestrians and demonstrates substantial performance improvement.
