Towards a general framework for the annotation of dance motion sequences

Raheb, Katerina El; Buccoli, Michele; Zanoni, Massimiliano; Katifori, Akrivi; Kasomoulis, Aristotelis; Sarti, Augusto; Ioannidis, Yannis

doi:10.1007/s11042-022-12602-y

Cited by 5 publications

(5 citation statements)

References 53 publications

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“…Dance movement is a continuous activity and selecting part of the movement for the user to characterize removes it from its original context, what happens before and after the segment, that might affect their annotation. As reported also in previous work, not only revisiting but comparing segments is useful when it comes to qualitative aspects of movement (El Raheb et al, 2022), during the interviews 7 participants said that they would like to change their answers after seeing another segment that seemed to be a closer match, comparing between them as to which was, for example, more airy.…”

Section: Discussionsupporting

confidence: 58%

“…Many researchers, in collaboration with dance researchers and artists, have worked on developing tools to manually characterize or algorithms to automatically compute the qualitative aspects of movement, i.e., not only what the body does (e.g., running or turning or raising an arm) but how this movement is done (e.g., fluently, heavily, etc.) (Alaoui et al, 2013;Camurri et al, 2016;El Raheb et al, 2022). The need for characterizing recorded or motioncaptured movement through the enrichment with verbal or other tags is emerging into a challenging interdisciplinary field that can serve a variety of purposes such as learning and teaching rhythm (dos Santos et al, 2018); assigning labels and descriptive tags on dance content to help with the memorization of step sequences by dancers (Alaoui et al, 2014); becoming part of a choreographic process (Leach, 2014;Blades, 2015;Ribeiro et al, 2016); and facilitating an interdisciplinary dialogue among different dance genres and cultures (El Raheb and Ioannidis, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Blazing fire or breezy wind? A story-driven playful experience for annotating dance movement

Kougioumtzian¹,

Raheb²,

Katifori³

et al. 2022

Front. Comput. Sci.

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The annotation of animated motion-captured segments is a challenging, interdisciplinary task, especially when it comes to characterizing movement qualitatively. The lack of intuitive, easy-to-learn-and-use frameworks is considered to be one of the biggest challenges in this process; another is the lack of approaches able to motivate a wide audience of users, from the broader public to dance experts, researchers and performers, to contribute with annotations. In this paper we present Motion Hollow, a story-driven playful experience that uses metaphors based on Laban Movement Analysis, an established framework for movement analysis and annotation, to familiarize novice users with the process of qualitative characterization of dance moves. This work proposes a first step into introducing movement annotation to non-expert users, and as such, its main goal is to explore the implications and potential of such an approach. The evaluation of the experience confirms its potential to transform the annotation of dance movement segments into an engaging and enjoyable experience as well as to foster a deeper understanding of movement annotation both as a concept and process.

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Section: Discussionsupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Blazing fire or breezy wind? A story-driven playful experience for annotating dance movement

Kougioumtzian¹,

Raheb²,

Katifori³

et al. 2022

Front. Comput. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…It also tries to optimize for computational cost for both manual and semiautomatic labeling. Other recent methods are [42].…”

Section: Related Workmentioning

confidence: 99%

Motion-region annotation for complex videos via label propagation across occluders

Mahmood

Diéz

Oliver

et al. 2022

Machine Vision and Applications

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Motion cue is pivotal in moving object analysis, which is the root for motion segmentation and detection. These preprocessing tasks are building blocks for several applications such as recognition, matching and estimation. To devise a robust algorithm for motion analysis, it is imperative to have a comprehensive dataset to evaluate an algorithm’s performance. The main limitation in making these kind of datasets is the creation of ground-truth annotation of motion, as each moving object might span over multiple frames with changes in size, illumination and angle of view. Besides the optical changes, the object can undergo occlusion by static or moving occluders. The challenge increases when the video is captured by a moving camera. In this paper, we tackle the task of providing ground-truth annotation on motion regions in videos captured from a moving camera. With minimal manual annotation of an object mask, we are able to propagate the label mask in all the frames. Object label correction based on static and moving occluder is also performed by applying occluder mask tracking for a given depth ordering. A motion annotation dataset is also proposed to evaluate algorithm performance. The results show that our cascaded-naive approach provides successful results. All the resources of the annotation tool are publicly available at http://dixie.udg.edu/anntool/.

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“…Dancesport is a classic sport that combines art and sports, and it contains rich connotations that are worth exploring [1][2]. At present, the teaching of dancesport in universities is still based on the teaching mode of "teaching and educating people", with "imparting knowledge" as the main task.…”

Section: Introductionmentioning

confidence: 99%

Classroom Model of DanceSport in Universities Based on Internet Layered Teaching Method

2023

FER

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The purpose of offering dancesport courses is to cultivate sports talents with strong professional and comprehensive qualities. Layered teaching methods have been applied in many disciplines and have achieved significant results, effectively solving the problem of imbalanced classroom teaching caused by individual differences. Therefore, the use of hierarchical teaching methods in university dancesport teaching is an innovative and developmental teaching method. The purpose of this article is to study the classroom mode of dancesport in universities based on the Internet layered teaching method. Combining with the current situation of dancesport teaching in universities, focus has been on the research of dancesport teaching models based on the Internet layered teaching method, in order to make up for the shortcomings of traditional teaching models. Taking M Sports University as a case study, a teaching model was constructed based on multiple factors and the scientific and effective nature of the model was verified through rigorous teaching experiments. Through a comprehensive analysis of a series of evaluation feedback information, it was found that 70% of students are very receptive to the application of internet layered teaching in university dancesport classes.

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Towards a general framework for the annotation of dance motion sequences

Cited by 5 publications

References 53 publications

Blazing fire or breezy wind? A story-driven playful experience for annotating dance movement

Blazing fire or breezy wind? A story-driven playful experience for annotating dance movement

Motion-region annotation for complex videos via label propagation across occluders

Classroom Model of DanceSport in Universities Based on Internet Layered Teaching Method

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