ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술지 Nonparametric Feature Matching Based Conditional Random Fields for Gesture Recognition from Multi-Modal Video
Cited 19 time in scopus Download 6 time Share share facebook twitter linkedin kakaostory
저자
장주용
발행일
201608
출처
IEEE Transactions on Pattern Analysis and Machine Intelligence, v.38 no.8, pp.1612-1625
ISSN
0162-8828
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TPAMI.2016.2519021
협약과제
15MS3500, 인터랙티브 콘텐츠와 상호작용을 위한 고정밀 모바일 및 파노라믹 360도 다수 사용자 동작 인식 기술 개발, 박지영
초록
We present a new gesture recognition method that is based on the conditional random field (CRF) model using multiple feature matching. Our approach solves the labeling problem, determining gesture categories and their temporal ranges at the same time. A generative probabilistic model is formalized and probability densities are nonparametrically estimated by matching input features with a training dataset. In addition to the conventional skeletal joint-based features, the appearance information near the active hand in an RGB image is exploited to capture the detailed motion of fingers. The estimated likelihood function is then used as the unary term for our CRF model. The smoothness term is also incorporated to enforce the temporal coherence of our solution. Frame-wise recognition results can then be obtained by applying an efficient dynamic programming technique. To estimate the parameters of the proposed CRF model, we incorporate the structured support vector machine (SSVM) framework that can perform efficient structured learning by using large-scale datasets. Experimental results demonstrate that our method provides effective gesture recognition results for challenging real gesture datasets. By scoring 0.8563 in the mean Jaccard index, our method has obtained the state-of-the-art results for the gesture recognition track of the 2014 ChaLearn Looking at People (LAP) Challenge.
키워드
conditional random field, Gesture recognition, nonparametric estimation, structured learning
KSP 제안 키워드
Appearance information, CRF model, Conditional Random Field(CRF), Dynamic programming technique, Feature matching, Generative probabilistic model, Gesture dataset, Gesture recognition, Input features, Jaccard index, Large-scale datasets