Action Recognition System Integrating Motion and Object Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F24%3A00377218" target="_blank" >RIV/68407700:21730/24:00377218 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-72359-9_19" target="_blank" >https://doi.org/10.1007/978-3-031-72359-9_19</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-72359-9_19" target="_blank" >10.1007/978-3-031-72359-9_19</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Action Recognition System Integrating Motion and Object Detection

Popis výsledku v původním jazyce

In this paper, we present a novel action recognition system based on the integration of information from two separate modules. The first module is responsible for motion detection and categorization. The second module is an instance segmentation module that recognizes objects and their positions in the scene. The information from both modules is integrated in the third module that recognizes the actions based on motion and object detection. Compared to the traditional systems based on motion detection, we are able to recognize fake actions (gestures) where no contextual objects are presented in the scene. Moreover, we detect the average motion speed of contextual objects to increase the precision of detected actions. We create a dataset of eight action types that include assembly actions with tools and also corresponding fake actions that have similar motion but where no tools are used. Our recognition system achieves 95.21% accuracy in this dataset compared to 85.52% for a system based on motion detection only. We demonstrate that combining data from two different sources can improve the overall results of the action recognition task. Our recognition system can be adopted in real world tasks to distinguish between real actions and gestures

Název v anglickém jazyce

Action Recognition System Integrating Motion and Object Detection

Popis výsledku anglicky

In this paper, we present a novel action recognition system based on the integration of information from two separate modules. The first module is responsible for motion detection and categorization. The second module is an instance segmentation module that recognizes objects and their positions in the scene. The information from both modules is integrated in the third module that recognizes the actions based on motion and object detection. Compared to the traditional systems based on motion detection, we are able to recognize fake actions (gestures) where no contextual objects are presented in the scene. Moreover, we detect the average motion speed of contextual objects to increase the precision of detected actions. We create a dataset of eight action types that include assembly actions with tools and also corresponding fake actions that have similar motion but where no tools are used. Our recognition system achieves 95.21% accuracy in this dataset compared to 85.52% for a system based on motion detection only. We demonstrate that combining data from two different sources can improve the overall results of the action recognition task. Our recognition system can be adopted in real world tasks to distinguish between real actions and gestures

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

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Artificial Neural Networks and Machine Learning – ICANN 2024 33rd International Conference on Artificial Neural Networks, Lugano, Switzerland, September 17–20, 2024, Proceedings, 15025 LNCS

ISBN

978-3-031-72359-9

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

11

Strana od-do

259-269

Název nakladatele

Springer, Cham

Místo vydání

—

Místo konání akce

Lugano-Viganello

Datum konání akce

17. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001331898500019