Recent methods for people detection in overhead, fisheye images either use radially-aligned bounding boxes to represent people, assuming people always appear along image radius or require significant pre-/post-processing which radically increases computational complexity. In this work, we develop an end-to-end rotation-aware people detection method, named RAPiD, that detects people using arbitrarily-oriented bounding boxes. Our fully-convolutional neural network directly regresses the angle of each bounding box using a periodic loss function, which accounts for angle periodicities. We have also created a new dataset with spatio-temporal annotations of rotated bounding boxes, for people detection as well as other vision tasks in overhead fisheye videos. We show that our simple, yet effective method outperforms state-of-the-art results on three fisheye-image datasets. Code and dataset are available at http://vip.bu.edu/rapid .
@article{arxiv.2005.11623,
title = {RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images},
author = {Zhihao Duan and M. Ozan Tezcan and Hayato Nakamura and Prakash Ishwar and Janusz Konrad},
journal= {arXiv preprint arXiv:2005.11623},
year = {2020}
}