People detection is commonly used in computer vision systems, particularly for video surveillance and passenger flow statistics. Unlike standard cameras, fisheye cameras offer a large field of view and reduce occlusions when mounted overhead. However, due to the orientation variation of people in fisheye images, head detection models suffer from severe distortion when applied to fisheye images captured by top-view fisheye cameras. This work develops an end-to-end head detection method named HD-YOLO against complex situations in top-view fisheye images. The radius-aware loss function is designed to make HD-YOLO adapt to the impact of fisheye distortion, and the channel attention module is added to the model. We have also created new fisheye-image datasets for evaluation. Experiments showed that HD-YOLO outperforms other baseline methods on public and self-built datasets.
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