Skip to Main Content
Recently, laser-based people-tracking systems have received increasing attention for their ability to localize human subjects precisely in crowded situations. However, in a real environment, there exist many kinds of moving objects other than people, and previous methods have focused only on humans. To design a more sophisticated system, it is necessary to distinguish humans from observed objects and recognize their individual condition-for example, the kind and amount of belongings they are carrying. However, in previous methods using 2D laser range finders (LRFs), it proved difficult to recognize the type of target since all sensors observe a common horizontal plane and only the 2D contours of their targets. In this study, to recognize the type of target, we observe the 3D shapes of objects moving in their environment by installing LRFs with an angle of inclination. So far, in the area of 3D modeling, LRFs have been used to construct 3D models of static objects by moving the sensor and registering multiple views. In contrast, our method observes moving objects by using a static LRF network in the environment. Experimental results are shown to confirm the effectiveness of the proposed method.