Abstract:In recent years, the convolution neural networks in the field of pedestrian detection have achieved similar and even better results, compared to other methods. However, the slow detection speed can't meet the realistic demand. To solve this problem, a real-time pedestrian detection method is put forward. The scattered detection processes are integrated into a single depth network model. Images which can be calculated through the model can directly output detection results. The extended ETH dataset is used for training and testing the model. The experimental results show that the method is very fast and can achieve the goal of real-time detection with the guaranteed accuracy.