Keypose synthesis from 3D motion capture data by using evolutionary clustering

Günen M. A., Beşdok P. Ç., BEŞDOK E.

Concurrency and Computation: Practice and Experience, vol.34, no.1, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 34 Issue: 1
  • Publication Date: 2022
  • Doi Number: 10.1002/cpe.6485
  • Journal Name: Concurrency and Computation: Practice and Experience
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Keywords: computer vision, evolutionary search algorithms, keypose, motion capture, motion segmentation, photogrammetry, NUMERICAL FUNCTION OPTIMIZATION, IMPULSIVE NOISE SUPPRESSION, HUMAN POSE ESTIMATION, ALGORITHM, SEARCH, KINEMATICS, TRACKING, SELECTION, VISION, SPACE
  • Kayseri University Affiliated: No


© 2021 John Wiley & Sons Ltd.Motion Capture datasets are captured as high-frequency discrete samples using photogrammetric computer vision or various industrial geodetic measurement methods over the relevant model. Because Motion Capture data are inherently large-datasets, expressing Motion Capture data without employing a motion data abstraction approach such as keypose is challenging. Keypose synthesis is a serious problem in many applications. Unfortunately, the local statistical features of Motion Capture data over time periods vary often, making it difficult to determine a key point summing the motion activation involved. Conventional clustering methods, such as Fuzzy C-Means (FCM), that are sensitive to initial conditions, can be fitted to a local solution rather than producing a highly reliable keypose. Data clustering can be performed using evolutionary search methods without being limited to relatively local solutions. In this paper, the Motion Capture data were clustered using evolutionary search algorithms, and related keyposes were synthesized using the “minimum-distance to cluster-centers” principle. In Experiments section of this paper, motion data containing sportive movements were clustered by using evolutionary algorithms (i.e., Particle Swarm Optimization, Artificial Bee Colony, and Differential Search Algorithm) and classical clustering algorithms (i.e., Self-Organizing Neural Network, FCM) to obtain related keyposes. The computed statistics exposed that evolutionary methods were more successful in obtaining keypose than classical methods.