A bidirectional invariant representation of motion for gesture recognition and reproduction

Soloperto, Raffaele; Saveriano, Matteo; Lee, Dongheui

doi:10.1109/icra.2015.7140062

Cited by 12 publications

(18 citation statements)

References 10 publications

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“…We will investigate the performace of FADE in this case compared with stateof-the-art approaches like [6]. Another research direction will consist in combining FADE with skeleton-free invariant representations [29] of marker and torso movements.…”

Section: Discussionmentioning

confidence: 99%

Encoding human actions with a frequency domain approach

Shah

Falco

Saveriano

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-In this work, we propose a Frequency-based Action Descriptor (FADE) to represent human actions. In robotics, with the development of Programming by Demonstration (PbD) methods, representing and recognizing large sets of actions has become crucial to build autonomous systems that learn from humans. The FADE descriptor leverages Fast Fourier Transform (FFT) for action representation and is combined with the Manhattan distance for measuring similarities between actions. It is characterized by a low time and space complexity and is particularly suitable for classification of human actions. For clustering problems, we propose a modified version of FADE, called Uncompressed-FADE (U-FADE), which performs well in combination with Spectral Clustering algorithms at the price of a reduced compression. We compare FADE with action descriptors based on Singular Value Decomposition (SVD) and Hidden Markov Models (HMM) on the entire HDM05 motion capture database. Despite the high dimensionality of the problem, we obtained on the entire database a promising recognition rate of 78% combining FADE with a simple 1-NN classification algorithm. Furthermore, we achieved a rate of 98% on a small action set and 88% on a medium action set.

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Section: Discussionmentioning

confidence: 99%

Encoding human actions with a frequency domain approach

Shah

Falco

Saveriano

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…However, the rigid body coordinate system may cannot be consecutively extracted from all time instances due to occlusions. Some works denote the Frenet-Serret frame [6], [12] as the local coordinate system, which can be built according to the first-order and second-order time derivatives of the motion trajectory. Although it can be acquired across all time instances, the Frenet-Serret frame is sensitive to noise, speed variations, and local perturbations of the trajectory.…”

Section: ) Shape Analysis Of 3-d Curvesmentioning

confidence: 99%

“…1) RRV-l 2 +DTW: The standard l 2 -norm is to measure the distance between two RRV descriptors. 2) RRV-New+DTW: The distance between two RRV descriptors is measured by the proposed metric as in (12). The setups 1) and 2) aim to demonstrate the effectiveness of the new metric.…”

Section: A Experimental Setup 1) Australian Sign Language (Auslan2) mentioning

confidence: 99%

RRV: A Spatiotemporal Descriptor for Rigid Body Motion Recognition

Guo

Shao

2018

IEEE Trans. Cybern.

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The motion behaviors of a rigid body can be characterized by a six degrees of freedom motion trajectory, which contains the 3-D position vectors of a reference point on the rigid body and 3-D rotations of this rigid body over time. This paper devises a rotation and relative velocity (RRV) descriptor by exploring the local translational and rotational invariants of rigid body motion trajectories, which is insensitive to noise, invariant to rigid transformation and scale. The RRV descriptor is then applied to characterize motions of a human body skeleton modeled as articulated interconnections of multiple rigid bodies. To show the descriptive ability of our RRV descriptor, we explore its potentials and applications in different rigid body motion recognition tasks. The experimental results on benchmark datasets demonstrate that our RRV descriptor learning discriminative motion patterns can achieve superior results for various recognition tasks.

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“…The accuracy is 96.0% for the action jumping (1), which has been confused in 4.0% of cases with the action jumping jacks (2). Moreover, the action sit down (10) presents a recognition rate of 92.0%, since it is confused in 4.0% of cases with stand up (9), and in 4.0% of cases with sit down and stand up (11). The action sit down and stand up achieves 96.0% accuracy and it is confused with sit down in 4.0% of cases.…”

Section: Comparison With Angle-based Approachesmentioning

confidence: 99%

A Human Action Descriptor Based on Motion Coordination

Falco

Saveriano

Hasany

et al. 2017

IEEE Robot. Autom. Lett.

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In this paper, we present a descriptor for human whole-body actions based on motion coordination. We exploit the principle, well known in neuromechanics, that humans move their joints in a coordinated fashion. Our coordination-based descriptor (CODE) is computed by two main steps. The first step is to identify the most informative joints which characterize the motion. The second step enriches the descriptor considering minimum and maximum joint velocities and the correlations between the most informative joints. In order to compute the distances between action descriptors, we propose a novel correlation-based similarity measure. The performance of CODE is tested on two public datasets, namely HDM05 and Berkeley MHAD, and compared with stateof-the-art approaches, showing recognition results.Index Terms-Gesture, human factors and human-in-the-loop, human-centered automation, posture and facial expressions.

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A bidirectional invariant representation of motion for gesture recognition and reproduction

Cited by 12 publications

References 10 publications

Encoding human actions with a frequency domain approach

Encoding human actions with a frequency domain approach

RRV: A Spatiotemporal Descriptor for Rigid Body Motion Recognition

A Human Action Descriptor Based on Motion Coordination

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