Selection of pose configuration parameters of motion capture data based on dynamic time warping

Świtoński, Adam; Josiński, Henryk; Zghidi, Hafed; Wojciechowski, Konrad

doi:10.1063/1.4912879

Cited by 3 publications

(10 citation statements)

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“…To detect main gait cycle containing two middle adjacent steps, tracking of distances between feet is carried out. The same database was also used in [1]. The default skeleton model of Vicon Blade software was applied as presented in Fig.…”

Section: Collected Gait Databasementioning

confidence: 99%

“…1. Similar as in [1], to minimize the influence of the calibration process of the motion capture measurements on the final recognition, the acquisition is divided into sessions. The calibration is carried out independently in every session.…”

Section: Collected Gait Databasementioning

confidence: 99%

“…It allows to assess their discriminative features. Obtained results are compared to the corresponding ones obtained for skeleton motion data, presented in the previous ICNAAM conference [1].…”

Section: Introductionmentioning

confidence: 99%

“…In our previous works Dynamic Time Warping (DTW) transform was applied to classification of skeleton based motion data [1,2]. They are represented by time sequences of relative rotations performed by subsequent joints, coded in a form of Euler angles or unit quaternions.…”

Section: Introductionmentioning

confidence: 99%

“…The obtained results were satisfactory. For highly precise motion capture measurements and applied validation dataset with 30 classes, over than 90% accuracy is achieved [1]. However in a case of markerless acquisition with much greater noise, it was necessary to consider some additional pose parameters to improve the classification [2].…”

Section: Introductionmentioning

confidence: 99%

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Motion data classification on the basis of dynamic time warping with a cloud point distance measure

Świtoński

Josiński

Zghidi

et al. 2016

AIP Conference Proceedings

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The paper deals with the problem of classification of model free motion data. The nearest neighbors classifier which is based on comparison performed by Dynamic Time Warping transform with cloud point distance measure is proposed. The classification utilizes both specific gait features reflected by a movements of subsequent skeleton joints and anthropometric data. To validate proposed approach human gait identification challenge problem is taken into consideration. The motion capture database containing data of 30 different humans collected in Human Motion Laboratory of Polish-Japanese Academy of Information Technology is used. The achieved results are satisfactory, the obtained accuracy of human recognition exceeds 90%. What is more, the applied cloud point distance measure does not depend on calibration process of motion capture system which results in reliable validation.

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Section: Collected Gait Databasementioning

confidence: 99%

Section: Collected Gait Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Motion data classification on the basis of dynamic time warping with a cloud point distance measure

Świtoński

Josiński

Zghidi

et al. 2016

AIP Conference Proceedings

Self Cite

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Recognition of Human Gestures Represented by Depth Camera Motion Sequences

Świtoński

Piórkowski

Josiński

et al. 2015

New Trends in Intelligent Information and Database Systems

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Human Actions Analysis: Templates Generation, Matching and Visualization Applied to Motion Capture of Highly-Skilled Karate Athletes

Hachaj

Piekarczyk

Ogiela

2017

Sensors

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The aim of this paper is to propose and evaluate the novel method of template generation, matching, comparing and visualization applied to motion capture (kinematic) analysis. To evaluate our approach, we have used motion capture recordings (MoCap) of two highly-skilled black belt karate athletes consisting of 560 recordings of various karate techniques acquired with wearable sensors. We have evaluated the quality of generated templates; we have validated the matching algorithm that calculates similarities and differences between various MoCap data; and we have examined visualizations of important differences and similarities between MoCap data. We have concluded that our algorithms works the best when we are dealing with relatively short (2–4 s) actions that might be averaged and aligned with the dynamic time warping framework. In practice, the methodology is designed to optimize the performance of some full body techniques performed in various sport disciplines, for example combat sports and martial arts. We can also use this approach to generate templates or to compare the correct performance of techniques between various top sportsmen in order to generate a knowledge base of reference MoCap videos. The motion template generated by our method can be used for action recognition purposes. We have used the DTW classifier with angle-based features to classify various karate kicks. We have performed leave-one-out action recognition for the Shorin-ryu and Oyama karate master separately. In this case, 100% actions were correctly classified. In another experiment, we used templates generated from Oyama master recordings to classify Shorin-ryu master recordings and vice versa. In this experiment, the overall recognition rate was 94.2%, which is a very good result for this type of complex action.

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Selection of pose configuration parameters of motion capture data based on dynamic time warping

Cited by 3 publications

References 0 publications

Motion data classification on the basis of dynamic time warping with a cloud point distance measure

Motion data classification on the basis of dynamic time warping with a cloud point distance measure

Recognition of Human Gestures Represented by Depth Camera Motion Sequences

Human Actions Analysis: Templates Generation, Matching and Visualization Applied to Motion Capture of Highly-Skilled Karate Athletes

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