Action Recognition System Integrating Motion and Object Detection
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Action Recognition System Integrating Motion and Object Detection
Original language description
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
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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
Number of pages
11
Pages from-to
259-269
Publisher name
Springer, Cham
Place of publication
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Event location
Lugano-Viganello
Event date
Sep 17, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001331898500019