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In this paper, we propose a novel unified framework for unsupervised object individuation from RGB-D image sequences. The proposed framework integrates existing location-based and feature-based object segmentation methods to achieve both computational efficiency and robustness in unstructured and dynamic situations. Based on the infant's object indexing theory, the newly proposed ambiguity graph plays...
Operating in unstructured dynamic human environments, it is desirable for a robot to identify dynamic objects and robustly track them without prior knowledge. This paper proposes a novel model-free approach for probabilistic representation and tracking of moving objects from 3D point set data based on Gaussian Mixture Model (GMM). GMM is inherently flexible such that represents any shape of objects...
In this paper, we consider the problem of tracking human motion with a 22-DOF kinematic model from depth images. In contrast to existing approaches, our system naturally scales to multiple sensors. The motivation behind our approach, termed Multiple Depth Camera Approach (MDCA), is that by using several cameras, we can significantly improve the tracking quality and reduce ambiguities as for example...
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