In this paper, we propose a novel multiple object tracking method from RGB-D point set data by introducing the hierarchical spatiotemporal data association method (HSTA) in order to robustly track multiple objects without prior knowledge. HSTA is able to construct not only temporal associations between multiple objects, but also component-level spatiotemporal associations that allow the correction of falsely detected objects in the presence of various types of interaction among multiple objects. The proposed method was evaluated using the four representative interaction cases such as split, complete occlusion, partial occlusion, and multiple contacts. As a result, HSTA showed significantly more robust performance than did other temporal data association methods in the experiments.