Real-Time Hand Posture Recognition for Human-Robot Interaction Tasks

In this work, we present a multiclass hand posture classifier useful for human-robot interaction tasks. The proposed system is based exclusively on visual sensors, and it achieves a real-time performance, whilst detecting and recognizing an alphabet of four hand postures. The proposed approach is ba...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2016-01, Vol.16 (1), p.36-36
Main Authors: Hernandez-Belmonte, Uriel Haile, Ayala-Ramirez, Victor
Format: Article
Language:English
Subjects:
Accuracy
AdaBoost
Algorithms
bootstrapping
Classifiers
Cluster Analysis
Datasets
Deformation
Detectors
Formability
full image evaluation
Hand - physiology
human-robot interaction
Humans
Image Processing, Computer-Assisted - methods
Man-Machine Systems
Methodology
Methods
Pattern Recognition, Automated - methods
Performance evaluation
Posture - physiology
Real time
Robotics - methods
Robots
Sensors
Tasks
vision-based hand posture recognition
Wearable computers
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Summary:In this work, we present a multiclass hand posture classifier useful for human-robot interaction tasks. The proposed system is based exclusively on visual sensors, and it achieves a real-time performance, whilst detecting and recognizing an alphabet of four hand postures. The proposed approach is based on the real-time deformable detector, a boosting trained classifier. We describe a methodology to design the ensemble of real-time deformable detectors (one for each hand posture that can be classified). Given the lack of standard procedures for performance evaluation, we also propose the use of full image evaluation for this purpose. Such an evaluation methodology provides us with a more realistic estimation of the performance of the method. We have measured the performance of the proposed system and compared it to the one obtained by using only the sampled window approach. We present detailed results of such tests using a benchmark dataset. Our results show that the system can operate in real time at about a 10-fps frame rate.
ISSN:1424-8220
1424-8220
DOI:10.3390/s16010036