Pedestrian and Part Position Detection using a Regression-based Multiple Task Deep Convolutional Neural Network

In driving support systems, it is not only necessary to detect the position of pedestrians, but also to estimate the distance between a pedestrian and the vehicle. In general approaches using monocular cameras, the upper and lower positions of each pedestrian are detected using a bounding box obtained from a pedestrian detection technique. The distance between the pedestrian and the vehicle is then estimated using these positions and the camera parameters. This conventional framework uses independent pedestrian detection and position detection processes to estimate the distance. In this paper, we propose a method to detect both the pedestrian and their position simultaneously using a regression-based deep convolutional neural network (DCNN). This simultaneous detection method is possible to train efficient features for both tasks, because it is attention to head and leg regions from given labels. In the experiments, our method improves the performance of pedestrian detection compared with the DCNN which detects only pedestrian. The proposed approach also improves the detection accuracy of the head and leg positions compared with the methods that detect only these positions. Using the results of position detection and the camera parameters, our method achieves distance estimation to within 5% error.