DBSCAN for Hand Tracking and Gesture Recognition | Zendy

Wisnu Aditya | Zendy; Herman Tolle | Zendy; Timothy K. Shih | Zendy

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DBSCAN for Hand Tracking and Gesture Recognition

Author(s) -

Wisnu Aditya,

Herman Tolle,

Timothy K. Shih

Publication year - 2020

Publication title -

journal of information technology and computer science

Language(s) - English

Resource type - Journals

eISSN - 2540-9824

pISSN - 2540-9433

DOI - 10.25126/jitecs.202052174

Subject(s) - computer science , dbscan , artificial intelligence , cluster analysis , segmentation , gesture recognition , gesture , computer vision , tracking (education) , process (computing) , pattern recognition (psychology) , fuzzy clustering , canopy clustering algorithm , psychology , pedagogy , operating system

Hand segmentation and tracking are important issues for hand-gesture recognition. Using depth data, it can speed up the segmentation process because we can delete unnecessary data like the background of the image easily. In this research, we modify DBSCAN clustering algorithm to make it faster and suitable for our system. This method is used in both hand tracking and hand gesture recognition. The results show that our method performs well in this system. The proposed method can outperform the original DBSCAN and the other clustering method in terms of computational time.

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