3D Person Tracking In World Coordinates and Attribute Estimation with PDR

In this paper, we propose an online 3D person tracking method and an attribute estimation method with pedestrian dead reckoning (PDR). For person tracking, we employ a structured prediction approach, which extends the Struck algorithm. Although the main stream of visual object tracking, including Struck, utilizes only 2D information in image coordinates, it is difficult to track object correctly because of changes in the scale and angle of the target. In contrast, our classifier adaptively learns structural relationship in world coordinates and in image coordinates using Structured SVM. Furthermore, we combine visual tracking results and sensor trajectories based on PDR. Our method estimates a person attribute whether insider like a sales staff, or outsider like a customer. According to experimental results, the proposed method outperforms the existing methods regarding the quality of localization. In addition, experimental results show that our method can estimate the attribute at a ratio of 0.84.