Robust Pose Estimation of Pedestrians with a Deep Neural Networks

Abstract

In this paper, we provide a method for robust estimation of pedestrian pose that is especially useful for autonomous vehicles traveling toward pedestrians far away. Pedestrians in the far distance appear relatively small when seen by a camera, making it difficult to estimate the pedestrian's pose. We use fused deep neural networks (DNNs) to resolve the problems presented by pedestrians in the far distance. First, DNNs are used to detect pedestrians and enlarge the observed image. Next, the DNN method of pose estimation is applied. The proposed method uses a single camera to estimate the posture of a pedestrian in the far distance. Far-off pedestrians observed by cameras in moving cars appear as low-resolution images of non-rigid bodies. Detection and orientation estimation are difficult with conventional image processing methods. We used a series of DNNs to detect pedestrians, improve data availability, and estimate challenging postures to address these limitations. In this paper, we propose a method based on the multi-stage fusion of DNNs to solve a difficult problem for a single DNN. The experimental results established the superiority of the proposed method when applied to data challenging for conventional pose estimation methods. Applications of the proposed method include observing small objects and objects in the far distance. The method may be especially useful in surveillance systems, sports broadcasting, and other applications requiring human posture estimation.