Adversarial Training-Based Hard Example Mining for Pedestrian Detection in Fish-Eye Images

Since fish-eye cameras are popular in the intelligent transportation systems, accurate pedestrian detection in fish-eye images becomes more and more critical in low-speed scenarios. Usually, big data based training is the key for detectors to handle the distortion problem in fish-eye images. Especially, hard examples are more important for detectors. They have more complex features and are hard to recognize. In conventional methods, fish-eye images are collected and labeled manually. These methods are expensive and labor-intense. More importantly, these methods are still hard to collect abundant hard examples since they are rare in reality. This work proposes the Distortion Generation Network, which generates generous fish-eye images automatically using only small samples. Moreover, the Adversarial Distortion Generation Network is proposed to mine hard examples via adversarial training. These hard examples benefit detectors to be more robust to seriously distorted objects in fish-eye images. Experiments with the ETH, the KITTI, the GM-ATCI and real fish-eye datasets demonstrate that the proposed methods achieve higher accuracy than conventional methods in pedestrian detection in fish-eye images.