Naslov
Two-Model-Based Online Hand Gesture Recognition from Skeleton Data

Autori
Doždor, Zorana ; Hrkać, Tomislav ; Kalafatić, Zoran

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP / Radeva, Petia ; Farinella, Giovanni Maria ; Bouatouch, Kadi - Setúbal : SCITEPRESS, 2023, 838-845

ISBN
978-989-758-634-7

Skup
International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications

Mjesto i datum
Lisabon, Portugal, 19.02.2023. - 21.02.2023

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Recurrent Neural Network ; Gated Recurrent Unit ; Online Gesture Recognition ; Hand Skeleton ; Sliding Window

Sažetak
Hand gesture recognition from skeleton data has recently gained popularity due to the broad areas of application and availability of adequate input devices. However, before utilising this technology in real-world conditions there are still many challenges left to overcome. A major challenge is robust gesture localization – estimating the beginning and the end of a gesture in online conditions. We propose an online gesture detection system based on two models – one for gesture localization and the other for gesture classification. This approach is tested and compared against the one-model approach, often found in literature. The system is evaluated on the recent SHREC challenge which offers datasets for online gesture detection. Results show the benefits of distributing the tasks of localization and recognition instead of using one model for both tasks. The proposed system obtains state-of- the-art results on SHREC gesture detection dataset.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo

POVEZANOST RADA