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Free form motion trajectories prove to be an informative and compact motion clue in sketching long-term, spatiotemporal motions. Hence, motion trajectories have been used for characterizing human behaviors/activities, robot actions and other objects’ movements. However, it is observed that most of the previous studies merely use motion trajectories straightforwardly in the raw data form, which is...
In this paper, a novel 3-D motion trajectory signature is introduced to serve as an effective description to the raw trajectory. More importantly, based on the trajectory signature, a probabilistic model-based cluster signature is further developed for modeling a motion class. The cluster signature is a mixture model-based motion description that is useful for motion class perception, recognition...
In this paper, we propose a novel concept of motion descriptor to serve as a fundamental mechanism to describe free form and long-term spatiotemporal motions. The motions are characterized by the underlying motion trajectories extracted via visual tracking. The motion descriptor is a so-called signature description of the motion trajectories. We first present the rationale of the signature definition...
Robot learning by demonstration (LbD) has been a natural learning paradigm through which robots can learn the underlying tasks from human's demonstrations. In most LbD related work, the demonstration normally just includes simple and short-term actions like gestures and grasps. Little attention was paid on the demonstration learning of long-term motions. In this paper, we propose to study the motion...
Motion trajectory is a meaningful and informative clue in characterizing the motions of human, robots or moving objects. Hence, it is important to explore effective motion trajectory modeling. However, with the existing methods, a motion trajectory is used in its raw data form and effective trajectory description is lacking. In this paper, we propose a novel 3D motion trajectory signature descriptor...
In most reported works about robot learning by demonstration (LbD), the demonstration is normally limited to simple gestures or grasp actions. In this paper, motion trajectory oriented LbD is studied in which free form 3-D motion trajectory is extracted to characterize certain human demonstrations. We propose to build effective description to motion trajectories to be learned by a robot instead of...
Motion trajectory is a compact clue for motion characterization. However, it is normally used directly in its raw data form in most work and effective trajectory description is lacking. In this paper, we propose a novel hierarchical motion trajectory signature descriptor, which can not only fully capture motion features for detailed perception, but also can be used for probabilistic fast recognition...
Shape descriptor plays important role in model based subject (object or motion) recognition. A difficulty is that the projected views of the same subject may vary with respect to the changes of viewpoint (camera pose). Therefore, viewpoint invariant descriptors are desired. To this end, many geometric invariants based projective invariants have been studied. However, it is rather hard to use most...
Motion trajectory modeling plays important role in characterizing human or robot action and behavior. However, effective and capable descriptors are lacking that can fully depict space trajectories. In this paper, we propose a novel signature mechanism for free form trajectory modeling in Euclidean space. The signature admits rich invariants due to the computational locality. By implementing the approximate...
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