Segmentation and tracking of object when grasped and moved within living spaces

Object tracking from camera images is a key technique in the field of computer vision. Because the most representative application of this technique is security or surveillance, objects such as pedestrians or vehicles in public spaces are considered as tracking targets. However, it is also useful to track objects such as daily items found in indoor living spaces, including offices, private rooms, and other locations, when they go missing or need to be located. However, it is not known in advance which part of the living space can move as a single object to be tracked. Moreover, because objects in a living space do not move by themselves but are moved by human hands, the bodies of objects in motion are usually occluded by the hands for a relatively long period of time, and drastic changes in the position and appearance of the objects may occur after a long-term occlusion. In this article, we propose an approach to finding the correspondence between objects observed before and after their occlusion for object tracking purposes by detecting the grasp and release of such objects by the same hand. Segmentation of the body of each object is also realized through the detection of its grasp and release.