Pedestrian Detection in Fish-eye Images using Deep Learning: Combine Faster R-CNN with an effective Cutting Method

With the development of artificial intelligence, pedestrian detection has become an important research topic in the field of intelligent video surveillance. Fish-eye camera is a useful tool for video monitoring. However, due to the edge distortion of the fish-eye image, which puts higher requirements and challenges on the pedestrian detection technology of fish-eye images. In this paper, an effective method is proposed by rotating cutting to address the problem, a fish-eye image is divided into an edge portion and a center portion. The effectiveness and performance of our method is verified by the traditional pedestrian detection method HOG+SVM and the Faster R-CNN based on convolutional neural network. The experimental results demonstrate the efficacy of the proposed approach, and Faster R-CNN achieves better performance than traditional method.