Matrix Descriptor of Changes (MDC): Activity Recognition Based on Skeleton

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F18%3A10241530" target="_blank" >RIV/61989100:27240/18:10241530 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-030-01449-0_2" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-01449-0_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-01449-0_2" target="_blank" >10.1007/978-3-030-01449-0_2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Matrix Descriptor of Changes (MDC): Activity Recognition Based on Skeleton

Popis výsledku v původním jazyce

A new method called Matrix Descriptor of Changes (MDC) is introduced in this work for description and recognition of human activity from sequences of skeletons. The primary focus was on one of the main problems in this area which is different duration of activities; it is assumed that the beginning and the end are known. Some existing methods use bag of features, hidden Markov models, recurrent neural networks or straighten the time interval by different sampling so that each activity has the same number of frames to solve this problem. The essence of our method is creating one or more matrices with a constant size. The sizes of matrices depend on the vector dimension containing the per-frame low-level features from which the matrix is created. The matrices then characterize the activity, even if we assume that certain activities may have different durations. The principle of this method is tested with two types of input features: (i) 3D position of the skeleton joints and (ii) invariant angular features of the skeleton. All kinds of feature types are processed by MDC separately and, in the subsequent step, all the information gathered together as a feature vector are used for recognition by Support Vector Machine classifier. Experiments have shown that the results are similar to results of the state-of-the-art methods. The primary contribution of proposed method was creating a new simple descriptor for activity recognition with preservation of the state-of-the-art results. This method also has a potential for parallel implementation and execution. (C) 2018, Springer Nature Switzerland AG.

Název v anglickém jazyce

Matrix Descriptor of Changes (MDC): Activity Recognition Based on Skeleton

Popis výsledku anglicky

A new method called Matrix Descriptor of Changes (MDC) is introduced in this work for description and recognition of human activity from sequences of skeletons. The primary focus was on one of the main problems in this area which is different duration of activities; it is assumed that the beginning and the end are known. Some existing methods use bag of features, hidden Markov models, recurrent neural networks or straighten the time interval by different sampling so that each activity has the same number of frames to solve this problem. The essence of our method is creating one or more matrices with a constant size. The sizes of matrices depend on the vector dimension containing the per-frame low-level features from which the matrix is created. The matrices then characterize the activity, even if we assume that certain activities may have different durations. The principle of this method is tested with two types of input features: (i) 3D position of the skeleton joints and (ii) invariant angular features of the skeleton. All kinds of feature types are processed by MDC separately and, in the subsequent step, all the information gathered together as a feature vector are used for recognition by Support Vector Machine classifier. Experiments have shown that the results are similar to results of the state-of-the-art methods. The primary contribution of proposed method was creating a new simple descriptor for activity recognition with preservation of the state-of-the-art results. This method also has a potential for parallel implementation and execution. (C) 2018, Springer Nature Switzerland AG.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 11182

ISBN

978-3-030-01448-3

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

12

Strana od-do

14-25

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Poitiers

Datum konání akce

24. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—