Folk Dance Pattern Recognition Over Depth Images Acquired via Kinect Sensor

Protopapadakis, Eftychios; Grammatikopoulou, Athina; Doulamis, Anastasios; Grammalidis, N.

doi:10.5194/isprs-archives-xlii-2-w3-587-2017

Cited by 28 publications

(18 citation statements)

References 18 publications

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“…To solve detection correlations between channels, a generalized form of a stabilized higher-order linear dynamical system and the multidimensional signal is represented as a third-order tensor. The work of [22] focuses on the application of segmentation and classification algorithms to Kinect-captured depth images and videos of folkloric dances in order to identify key movements and gestures and compare them against database instances. However, that work considers individual joints for the analysis, rather than the entire body pose, attaining recognition rates up to 42% in the general case.…”

Section: Related Workmentioning

confidence: 99%

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Dance Pose Identification from Motion Capture Data: A Comparison of Classifiers

et al. 2018

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Abstract:In this paper, we scrutinize the effectiveness of classification techniques in recognizing dance types based on motion-captured human skeleton data. In particular, the goal is to identify poses which are characteristic for each dance performed, based on information on body joints, acquired by a Kinect sensor. The datasets used include sequences from six folk dances and their variations. Multiple pose identification schemes are applied using temporal constraints, spatial information, and feature space distributions for the creation of an adequate training dataset. The obtained results are evaluated and discussed.

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Section: Related Workmentioning

confidence: 99%

“…In our case the knn parameterization process considers the number of k nearest points, which was set to = 5, a similar adaptation as in [22]. The value provides a good tradeoff.…”

Section: Algorithms Setupmentioning

confidence: 99%

Dance Pose Identification from Motion Capture Data: A Comparison of Classifiers

et al. 2018

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“…In [28], an algorithm using no temporal information is presented, which is used by the Kinect sensor to quickly and accurately predict 3D positions of body joints from a single depth image. Many scholars used the Kinect sensor as a low-cost sensor for motion capture [2,29,30], as it provides real-time 3D skeleton tracking in dark and bright indoor areas (since it uses infra-red). However, it is almost useless in sunlight, because the IR structured lighting pattern gets completely lost in ambient IR.…”

Section: Depth Sensorsmentioning

confidence: 99%

“…Along with hunting or eating and drinking together, it cannot be denied that dancing has been a vital part of humans' life through the ages. Such flows in time can be better pictured by focusing on folk dances, as they are already considered an important part of ICH, directly connected to local culture and ethnic or other type of group identity [2]. These reasons suggest that the preservation of folk dances is more than significant.…”

Section: Introductionmentioning

confidence: 99%

Digitization and Visualization of Folk Dances in Cultural Heritage: A Review

et al. 2018

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According to UNESCO, cultural heritage does not only include monuments and collections of objects, but also contains traditions or living expressions inherited from our ancestors and passed to our descendants. Folk dances represent part of cultural heritage and their preservation for the next generations appears of major importance. Digitization and visualization of folk dances form an increasingly active research area in computer science. In parallel to the rapidly advancing technologies, new ways for learning folk dances are explored, making the digitization and visualization of assorted folk dances for learning purposes using different equipment possible. Along with challenges and limitations, solutions that can assist the learning process and provide the user with meaningful feedback are proposed. In this paper, an overview of the techniques used for the recording of dance moves is presented. The different ways of visualization and giving the feedback to the user are reviewed as well as ways of performance evaluation. This paper reviews advances in digitization and visualization of folk dances from 2000 to 2018.

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“…Kinect is one of the most recognizable sensors in this category and in the choreography context can be used for recording sequences of points in 3D space for body joints at certain moments in time. Several recent research papers in the literature make use of such sensors for dance analysis, for example educational dance applications using sensors and gaming technologies [11], trajectory interpretation [12], advanced skeletal joints tracking [13], action or activity recognition [14][15][16][17][18][19][20], key pose identification [21] and key pose analysis [22]. Apart from Kinect, another popular alternative motion capture system is VICON which is significantly more sophisticated and accurate [9,23,24].…”

Section: Introductionmentioning

confidence: 99%

Choreographic Pattern Analysis from Heterogeneous Motion Capture Systems Using Dynamic Time Warping

et al. 2019

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The convention for the safeguarding of Intangible Cultural Heritage (ICH) by UNESCO highlights the equal importance of intangible elements of cultural heritage to tangible ones. One of the most important domains of ICH is folkloric dances. A dance choreography is a time-varying 3D process (4D modelling), which includes dynamic co-interactions among different actors, emotional and style attributes, and supplementary elements, such as music tempo and costumes. Presently, research focuses on the use of depth acquisition sensors, to handle kinesiology issues. The extraction of skeleton data, in real time, contains a significant amount of information (data and metadata), allowing for various choreography-based analytics. In this paper, a trajectory interpretation method for Greek folkloric dances is presented. We focus on matching trajectories’ patterns, existing in a choreographic database, to new ones originating from different sensor types such as VICON and Kinect II. Then, a Dynamic Time Warping (DTW) algorithm is proposed to find out similarities/dissimilarities among the choreographic trajectories. The goal is to evaluate the performance of the low-cost Kinect II sensor for dance choreography compared to the accurate but of high-cost VICON-based choreographies. Experimental results on real-life dances are carried out to show the effectiveness of the proposed DTW methodology and the ability of Kinect II to localize dances in 3D space.

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Folk Dance Pattern Recognition Over Depth Images Acquired via Kinect Sensor

Cited by 28 publications

References 18 publications

Dance Pose Identification from Motion Capture Data: A Comparison of Classifiers

Dance Pose Identification from Motion Capture Data: A Comparison of Classifiers

Digitization and Visualization of Folk Dances in Cultural Heritage: A Review

Choreographic Pattern Analysis from Heterogeneous Motion Capture Systems Using Dynamic Time Warping

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