A Novel Vehicle Open Door Safety System Based on Cyclist Detection Using Fisheye Camera and Improved Deep Convolutional Generative Adversarial Nets

Due to the lack of observation of rear moving objects by the passenger or driver of a vehicle, accidents happen frequently when they open the door of the vehicle. In this paper, we propose a novel vehicle open door safety system based on cyclist detection using fisheye camera and Improved Deep Convolutional Generative Adversarial Nets (IDCGANs). First of all, a fisheye camera is automatically turned on and captures the rear information of the vehicle when the vehicle is stopping. After that, a simple and effective method based on longitude coordinate is used to correct the distorted images. Second, Improved Deep Convolutional Generative Adversarial Nets is used to generate sample for data training. As the limited training datasets of cyclist and lots of annotation information which takes much time and efforts, we use Improved Deep Convolutional Generative Adversarial Nets to generate synthesized images of cyclist and use them as the training data for cyclist detectors. At last, Faster R-CNN detector is employed to train and detect the cyclist. The system was tested on realistic experiments and reached 87.2% precision rate and 95.3% recall rate. The feasibility of the proposed system for vehicle open door safety is demonstrated through simulation and test results.