ARPD: Anchor-free Rotation-aware People Detection using Topview Fisheye Camera
Quan Nguyen Minh, Bang Le Van, Can Nguyen, Anh Le, Viet Dung Nguyen

TL;DR
ARPD introduces an anchor-free, rotation-aware, single-stage convolutional network for detecting arbitrarily oriented people in fish-eye images, improving speed and accuracy over existing methods.
Contribution
The paper presents ARPD, a novel anchor-free approach that directly predicts object centers, sizes, and orientations, effectively handling arbitrary rotations in fish-eye images.
Findings
Competitive detection accuracy with state-of-the-art methods
Significantly faster inference speed
Effective angle prediction with periodic loss function
Abstract
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…
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