Object recognition using depth information of a consumer depth camera

Many computer vision applications adopting consumer depth cameras have recently received much attention due to the availability at low prices and the potential benefits to provide more useful information, which can result in a higher accuracy (e.g., for object recognition). In this work, to address the problem of drinking activity recognition in vision-based Ambient Assisted Living by using depth information, we propose a novel pre-processing scheme on extracting useful depth information of a target object, such as a mug, from a histogram of a depth image. By analyzing the histogram, the specific area or shape of an object can be localized. Then, the extracted depth information that is mostly related to the object is used for the object recognition. In the experiments, we demonstrated that the proposed approach can achieve a higher accuracy than the existing methods in recognizing an object.