Authors:
Ken Yano
;
Takeshi Ogawa
;
Motoaki Kawanabe
and
Takayuki Suyama
Affiliation:
Advanced Telecommunications Research Institute International, Japan
Keyword(s):
Behavior Analysis, Gesture Recognition, Randomized Clustering Forests, Human Machine Interface.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Features Extraction
;
Human and Computer Interaction
;
Human-Computer Interaction
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Image Generation Pipeline: Algorithms and Techniques
;
Motion, Tracking and Stereo Vision
;
Optical Flow and Motion Analyses
Abstract:
Behavior recognition has been one of the hot topics in the field of computer vision and its application. The
popular appearance-based behavior classification methods often utilize sparse spatio-temporal features that
capture the salient features and then use a visual word dictionary to construct visual words. Visual word assignments
based on K-means clustering are very effective and behave well for general behavior classification.
However, these pipelines often demand high computational power for the stages for low visual feature extraction
and visual word assignment, and thus they are not suitable for real-time recognition tasks. To overcome
the inefficient processing of K-means and the nearest neighbor approach, an ensemble approach is used for fast
processing. For real-time recognition, an ensemble of random trees seems particularly suitable for visual dictionaries
owing to its simplicity, speed, and performance. In this paper, we focus on the real-time recognition
by utilizing a
random clustering forest and verifying its effectiveness by classifying various hand gestures. In
addition, we proposed a boosted random clustering forest so that training time can be successfully shortened
with minimal negative impact on its recognition rate. For an application, we demonstrated a possible use of
real-time gesture recognition by controlling a digital TV using hand gestures.
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