Relational databases for motion data

Riaz, Qaiser; Krüger, Björn; Weber, Andreas

doi:10.1504/ijica.2016.078723

Cited by 1 publication

(2 citation statements)

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“…To accelerate motion retrieval in large scale databases, Kovar and Gleicher [61] use match webs as an index structure to find numerically similar motions, Chai and Hodgins [17] build a graph to allow fast nearest neighbor search, while others decompose motions into body parts and use a hierarchical motion representation (R-trees and kd-trees) [28,56,63,68]. Another way to deal with time inconsistency in motion is to employ relational [75,82] or qualitative features [4,54,87]. Indeed, these features extract the dynamic and spatiotemporal information of motion, but not the numerical similarity between poses.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the large diversity of motions, and their complexity, makes automatic motion indexing and clustering challenging, especially for highly dynamic, heterogeneous, and stylized motions, such as dancing. Most motion clustering techniques rely on motion skeletal [13,56,62] or relational [54,75,82] information, and fail to assess some important aspects of human action, such as synchronization and scaling. In fact, each performer's improvisation, experience, and talent may result in different variations of the same dance, while the same dance can vary in the temporal order, duration, and time, even if they are performed by the same dancer.…”

Section: Introductionmentioning

confidence: 99%

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Digital Dance Ethnography

Aristidou

Shamir

Chrysanthou

2019

J. Comput. Cult. Herit.

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Folk dances often reflect the socio-cultural influences prevailing in different periods and nations; each dance produces a meaning, a story with the help of music, costumes and dance moves. However, dances have no borders; they have been transmitted from generation to generation, along different countries, mainly due to movements of people carrying and disseminating their civilization. Studying the contextual correlation of dances along neighboring countries, unveils the evolution of this unique intangible heritage in time, and helps in understanding potential cultural similarities. In this work we present a method for contextually motion analysis that organizes dance data semantically, to form the first digital dance ethnography. Firstly, we break dance motion sequences into some narrow temporal overlapping feature descriptors, named motion and style words, and then cluster them in a high-dimensional features space to define motifs. The distribution of those motion and style motifs creates motion and style signatures, in the content of a bag-of-motifs representation, that implies for a succinct but descriptive portrayal of motions sequences. Signatures are timescale and temporal-order invariant, capable of exploiting the contextual correlation between dances, and distinguishing fine-grained difference between semantically similar motions. We then use quartet-based analysis to organize dance data into a categorization tree, while inferred information from dance metadata descriptions are then used to set parent-child relationships. We illustrate a number of different organization trees, and portray the evolution of dances over time. The efficiency of our method is also demonstrated in retrieving contextually similar dances from a database.

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