  People detection in top-view, fish-eye images is challenging as people in
fish-eye images often appear in arbitrary directions and are distorted
differently. Due to this unique radial geometry, axis-aligned people detectors
often work poorly on fish-eye frames. Recent works account for this variability
by modifying existing anchor-based detectors or relying on complex
pre/post-processing. Anchor-based methods spread a set of pre-defined bounding
boxes on the input image, most of which are invalid. In addition to being
inefficient, this approach could lead to a significant imbalance between the
positive and negative anchor boxes. In this work, we propose ARPD, a
single-stage anchor-free fully convolutional network to detect arbitrarily
rotated people in fish-eye images. Our network uses keypoint estimation to find
the center point of each object and regress the object's other properties
directly. To capture the various orientation of people in fish-eye cameras, in
addition to the center and size, ARPD also predicts the angle of each bounding
box. We also propose a periodic loss function that accounts for angle
periodicity and relieves the difficulty of learning small-angle oscillations.
Experimental results show that our method competes favorably with
state-of-the-art algorithms while running significantly faster.
