RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images
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RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images

Sep 29, 2020
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Authors: Zhihao Duan, Ozan Tezcan, Hayato Nakamura, Prakash Ishwar, Janusz Konrad Description: 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. The source code for RAPiD is publicly available.

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