Household object management via integration of object movement detection from multiple cameras

This paper proposes an object movement detection method covering large areas of a room by using multiple cameras. When object movement detection for whole of a room is performed, there are several challenging difficulties: sizes of objects on the camera images are small, non-objects such as humans also exist on the images, objects are sometimes difficult to detect in specific viewpoints because of occlusion by humans or furniture or color similarity to near objects. In this work, to detect object movements robustly though the object sizes are small, we apply multiple view integration via features extracted from “stable changes” on each viewpoint. To discriminate between object and non-object, we focus on motion of changed regions. Our experiment in a room environment shows the multiple view integration method improves recall rate of object detection performance by about 0.2 when false positive rate is over 0.1.