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@inproceedings{1417925,
author = {Sedmidubský, Jan and Zezula, Pavel},
address = {Switzerland},
booktitle = {29th International Conference on Database and Expert Systems Applications (DEXA 2018)},
doi = {http://dx.doi.org/10.1007/978-3-319-98812-2_4},
editor = {S. Hartmann et al.},
keywords = {motion capture data; nearest-neighbor search; action recognition; action classification; re-ranking; similarity measure},
howpublished = {paměťový nosič},
language = {eng},
location = {Switzerland},
isbn = {978-3-319-98811-5},
pages = {50-65},
publisher = {Springer},
title = {Probabilistic Classification of Skeleton Sequences},
year = {2018}
}
TY - JOUR
ID - 1417925
AU - Sedmidubský, Jan - Zezula, Pavel
PY - 2018
TI - Probabilistic Classification of Skeleton Sequences
PB - Springer
CY - Switzerland
SN - 9783319988115
KW - motion capture data
KW - nearest-neighbor search
KW - action recognition
KW - action classification
KW - re-ranking
KW - similarity measure
N2 - Automatic classification of 3D skeleton sequences of human motions has applications in many domains, ranging from entertainment to medicine. The classification is a difficult problem as the motions belonging to the same class needn't be well segmented and can be performed by subjects of various body sizes in different styles and speeds. The state-of-the-art recognition approaches commonly solve this problem by training recurrent neural networks to learn the contextual dependency in both spatial and temporal domains. In this paper, we employ a distance-based similarity measure, based on deep convolutional features, to search for the k-nearest motions with respect to a query motion being classified. The retrieved neighbors are analyzed and re-ranked by additional measures that are automatically chosen for individual queries. The combination of deep features, dynamism in the similarity-measure selection, and a new kNN classifier brings the highest classification accuracy on a challenging dataset with 130 classes. Moreover, the proposed approach can promptly react to changing training data without any need for a retraining process.
ER -
SEDMIDUBSKÝ, Jan a Pavel ZEZULA. Probabilistic Classification of Skeleton Sequences. In S. Hartmann et al. \textit{29th International Conference on Database and Expert Systems Applications (DEXA 2018)}. Switzerland: Springer. s.~50-65. ISBN~978-3-319-98811-5. doi:10.1007/978-3-319-98812-2\_{}4. 2018.
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