Dance Gesture Recognition: A Survey

Devi, Mampi; Saharia, Sarat; Bhattacharyya, Dhruba K.

doi:10.5120/21696-4803

Cited by 21 publications

(7 citation statements)

References 33 publications

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“…Hsu developed a template selection method (minimum-maximum template selection method) based on the smallest to the largest class for the DTW recognizer to obtain a superior classification for improving recognition. Experimental results have successfully verified the effectiveness of DTW-based recognition algorithms for online handwriting and gesture recognition using inertial pens [30,31]. Despinoy proposed a new unsupervised algorithm that can automatically segment kinematic data in robot training sessions.…”

Section: Introductionmentioning

confidence: 83%

Digital Media Design for Dynamic Gesture Interaction with Image Processing

Xiong

Yong-guang

2022

Journal of Electrical and Computer Engineering

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With the rapid development of human-computer interaction technology and research in the fields of ergonomics and user experience, people have increasingly demanded robot availability and ease of use. The rapid development of modern computer computing and digital media technology has made people’s contact and interaction with computers more and more frequent, and they have appeared more in people’s daily lives. Therefore, the convenience and freedom of computer communication have been proposed. Based on the above background, the research content of this paper is the design of dynamic gesture interactive digital media based on image processing. This paper proposes a design scheme of an interactive touch system based on image processing and a dynamic gesture recognition method and uses the time difference method to experimentally simulate the system proposed in this paper. Image processing is the technique of analyzing images with a computer to achieve the desired results. Image processing technology generally includes three parts: image compression, enhancement and restoration, and matching, description, and recognition. The experimental results show that, for the same motion trajectory, the accuracy depends on the complexity of the gesture degree. And the system’s recognition accuracy rate is always maintained above 98%, confirming that the system has the performance requirements of high recognition accuracy, fast response speed, and stable work. Finally, the system was tested for performance and function, which verified that the system meets the real-time requirements. For a particular gesture operator, the accuracy rate has a great relationship with the standard degree of its operation. If the similarity with the model is high, you can achieve a high recognition rate.

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Section: Introductionmentioning

confidence: 83%

Digital Media Design for Dynamic Gesture Interaction with Image Processing

Xiong

Yong-guang

2022

Journal of Electrical and Computer Engineering

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“…Kinect cameras are often used to capture human motion [10] and dance [1], [4], [5], [11]. Camera Kinect been affordable (less than $ 150) and is very practical [10].…”

Section: Related Workmentioning

confidence: 99%

“…Dance is a movement class that very wide-ranging in many different styles and has many peculiarities [1]. Given this time, there are still many people who do not understand the kind of dance moves both classical dance and traditional and the meaning of each dance so it takes study on the introduction of the type of attitude dance movement that can be used for research in other fields, learning and assessment [2][3][4] and preserving the art and culture of Indonesian assets. Research on gesture recognition dance receive more attention worldwide [4] and have a huge impact on our daily lives [5].…”

Section: Introductionmentioning

confidence: 99%

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Gesture Recognition of Dance using Chain Code and Hidden Markov Model

Sutopo¹

2019

IJATCSE

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Dance one culture consists of motion. This paper seeks to recognize Golek Menak Dance movement to be easily studied from Indonesia, where the dance of the dancers (actor) is performed by using the motion capture Kinect sensor which then produces motion data format with Biovision Hierarchy (BVH), where data is a tensor which has position x, y, z. This research use test data Jogetan and Sabetan movement carried out featuring by Chain Code 15 (CC-15), which is a combination of 15 directions with forward motion (1), backward (-1) and fixed (0) to obtain vector quantization which is then carried by the gesture recognition using Hidden Markov Model (HMM). The novelty in this paper use Chain Code 15 (CC-15) to conduct the introduction featuring Dance with HMM classification, which produced an accuracy of 90% of ten (10) test data movement.

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“…Human gestures may include for instance standing, lying, bending, sitting, walking, jumping, etc. Human gesture recognition has been heavily studied because it plays an important role in human computer interaction applications [5], [6], [7], [8] such as health monitoring systems, surveillance systems, motion analysis in sports, and human behavior analysis.…”

Section: Introductionmentioning

confidence: 99%

Human gesture classification by brute-force machine learning for exergaming in physiotherapy

Deboeverie

Roegiers

Allebosch

et al. 2016

2016 IEEE Conference on Computational Intelligence and Games (CIG)

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Abstract-In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods.

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Dance Gesture Recognition: A Survey

Cited by 21 publications

References 33 publications

Digital Media Design for Dynamic Gesture Interaction with Image Processing

Digital Media Design for Dynamic Gesture Interaction with Image Processing

Gesture Recognition of Dance using Chain Code and Hidden Markov Model

Human gesture classification by brute-force machine learning for exergaming in physiotherapy

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