J
Junsong Yuan
Researcher at University at Buffalo
Publications - 471
Citations - 20391
Junsong Yuan is an academic researcher from University at Buffalo. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 59, co-authored 401 publications receiving 15651 citations. Previous affiliations of Junsong Yuan include Zhejiang University & Northwestern University.
Papers
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Proceedings ArticleDOI
Mining actionlet ensemble for action recognition with depth cameras
TL;DR: An actionlet ensemble model is learnt to represent each action and to capture the intra-class variance, and novel features that are suitable for depth data are proposed.
Proceedings ArticleDOI
Sparse reconstruction cost for abnormal event detection
Yang Cong,Junsong Yuan,Ji Liu +2 more
TL;DR: The method provides a unified solution to detect both local abnormal events and global abnormal events through a sparse reconstruction over the normal bases and extends it to support online abnormal event detection by updating the dictionary incrementally.
Journal ArticleDOI
Robust Part-Based Hand Gesture Recognition Using Kinect Sensor
TL;DR: A novel distance metric, Finger-Earth Mover's Distance (FEMD), is proposed, which only matches the finger parts while not the whole hand, it can better distinguish the hand gestures of slight differences.
Journal ArticleDOI
Learning Actionlet Ensemble for 3D Human Action Recognition
TL;DR: This paper proposes to characterize the human actions with a novel actionlet ensemble model, which represents the interaction of a subset of human joints, which is robust to noise, invariant to translational and temporal misalignment, and capable of characterizing both the human motion and the human-object interactions.
Proceedings ArticleDOI
Robust hand gesture recognition based on finger-earth mover's distance with a commodity depth camera
TL;DR: A novel distance metric for hand dissimilarity measure, called Finger-Earth Mover's Distance (FEMD), which only matches fingers while not the whole hand shape, can better distinguish hand gestures of slight differences.