RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images

• URL: http://arxiv.org/abs/2005.11623v1
• Date: Sat, 23 May 2020 23:47:18 GMT
• Title: RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images
• Authors: Zhihao Duan, M. Ozan Tezcan, Hayato Nakamura, Prakash Ishwar, Janusz Konrad
• Abstract summary: We develop an end-to-end-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 rotation function. We show that our simple, yet effective method outperforms state-of-the-art results on three fisheye-image datasets.
• Score: 13.290341167863495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 .

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