Gesture Recognition using Latent-Dynamic based Conditional Random Fields and Scalar Features

International Seminar on Mathematics, Science, and Computer Science Education 2016

数学;自然科学;计算机科学

Yulita, I.N.^1 ; Fanany, M.I.^2 ; Arymurthy, A.M.^2

Departemen Ilmu Komputer, Universitas Padjadjaran, Jl. Raya Bandung - Sumedang Km. 21, Sumedang

45363, Indonesia^1

Fakultas Ilmu Komputer, Universitas Indonesia, Kampus UI Depok, Depok

16424, Indonesia^2

关键词: Conditional models;    Conditional random field;    Dynamic conditional random fields;    External dynamics;    Internal structure;    Probabilistic graphical models;    Sequence data;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/812/1/012113/pdf
DOI  :  10.1088/1742-6596/812/1/012113

来源: IOP

【 摘 要 】

The need for segmentation and labeling of sequence data appears in several fields. The use of the conditional models such as Conditional Random Fields is widely used to solve this problem. In the pattern recognition, Conditional Random Fields specify the possibilities of a sequence label. This method constructs its full label sequence to be a probabilistic graphical model based on its observation. However, Conditional Random Fields can not capture the internal structure so that Latent-based Dynamic Conditional Random Fields is developed without leaving external dynamics of inter-label. This study proposes the use of Latent-Dynamic Conditional Random Fields for Gesture Recognition and comparison between both methods. Besides, this study also proposes the use of a scalar features to gesture recognition. The results show that performance of Latent-dynamic based Conditional Random Fields is not better than the Conditional Random Fields, and scalar features are effective for both methods are in gesture recognition. Therefore, it recommends implementing Conditional Random Fields and scalar features in gesture recognition for better performance.