Real-time classification of dance gestures from skeleton animation

Raptis, Michalis; Kirovski, Darko; Hoppe, Hugues

doi:10.1145/2019406.2019426

Cited by 268 publications

(167 citation statements)

References 22 publications

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“…1). The joint centers are obtained from a randomized decision forest algorithm and the data seem to be accurate enough to be used for postural assessment (Bonnechère et al, 2013a;Clark et al, 2012;Dutta, 2012;Martin et al, 2012;Raptis et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Using Kinect™ sensor in observational methods for assessing postures at work

2014

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a b s t r a c tThis paper examines the potential use of KinectÔ range sensor in observational methods for assessing postural loads. Range sensors can detect the position of the joints at high sampling rates without attaching sensors or markers directly to the subject under study. First, a computerized OWAS ergonomic assessment system was implemented to permit the data acquisition from KinectÔ and data processing in order to identify the risk level of each recorded postures. Output data were compared with the results provided by human observers, and were used to determine the influence of the sensor view angle relative to the worker. The tests show high inter-method agreement in the classification of risk categories (Proportion agreement index ¼ 0.89 k ¼ 0.83) when the tracked subject is facing the sensor. The camera's point of view relative to the position of the tracked subject significantly affects the correct classification of the postures. Although the results are promising, some aspects involved in the use of low-cost range sensors should be further studied for their use in real environments.

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Section: Introductionmentioning

confidence: 99%

Using Kinect™ sensor in observational methods for assessing postures at work

2014

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“…The authors of (Raptis et al, 2011) describe a gesture classification system for skeletal wireframe motion. A classifier was designed and trained to recognize certain gestures, among several dozen, in real-time and with high accuracy.…”

Section: Related Workmentioning

confidence: 99%

Folk Dance Pattern Recognition Over Depth Images Acquired via Kinect Sensor

Protopapadakis

Grammatikopoulou

Doulamis

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The possibility of accurate recognition of folk dance patterns is investigated in this paper. System inputs are raw skeleton data, provided by a low cost sensor. In particular, data were obtained by monitoring three professional dancers, using a Kinect II sensor. A set of six traditional Greek dances (without their variations) consists the investigated data. A two-step process was adopted. At first, the most descriptive skeleton data were selected using a combination of density based and sparse modelling algorithms. Then, the representative data served as training set for a variety of classifiers.

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“…When dealing specifically with skeletal data obtained with RGB-D devices, it can be seen that some joints are more important than others if pose or motion recognition is targeted (Raptis et al, 2011). There are several joints in the torso, as for instance the shoulders or the hips, which do not show an independent motion and rather move along with the whole body.…”

Section: Evolutionary Feature Subset Selectionmentioning

confidence: 99%

“…There are several joints in the torso, as for instance the shoulders or the hips, which do not show an independent motion and rather move along with the whole body. Therefore, taking this knowledge into account, the characteristic value of the motion can be retained, and at the same time dimensionality reduction can be performed in order to improve the performance of the classification (Raptis et al, 2011).…”

Section: Evolutionary Feature Subset Selectionmentioning

confidence: 99%

Evolutionary joint selection to improve human action recognition with RGB-D devices

Chaaraoui

Padilla-López

Climent-Pérez

et al. 2014

Expert Systems with Applications

138

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Interest in RGB-D devices is increasing due to their low price and the wide range of possible applications that come along. These devices provide a marker-less body pose estimation by means of skeletal data consisting of 3D positions of body joints. These can be further used for pose, gesture or action recognition. In this work, an evolutionary algorithm is used to determine the optimal subset of skeleton joints, taking into account the topological structure of the skeleton, in order to improve the final success rate. The proposed method has been validated using a state-of-the-art RGB action recognition approach, and applying it to the MSR-Action3D dataset. Results show that the proposed algorithm is able to significantly improve the initial recognition rate and to yield similar or better success rates than the state-of-the-art methods.

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Real-time classification of dance gestures from skeleton animation

Cited by 268 publications

References 22 publications

Using Kinect™ sensor in observational methods for assessing postures at work

Using Kinect™ sensor in observational methods for assessing postures at work

Folk Dance Pattern Recognition Over Depth Images Acquired via Kinect Sensor

Evolutionary joint selection to improve human action recognition with RGB-D devices

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