Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconstruction of articulated objects from point correspondences in a single uncalibrated image
Computer Vision and Image Understanding
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating anthropometry and pose from a single uncalibrated image
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Probabilistic Methods for Finding People
International Journal of Computer Vision
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
A Gesture Based Interface for Human-Robot Interaction
Autonomous Robots
Dynamical system representation, generation, and recognition of basic oscillatory motion gestures, and application for the control of actuated mechanisms
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Automated extraction and parameterization of motions in large data sets
ACM SIGGRAPH 2004 Papers
View independent human body pose estimation from a single perspective image
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Robust data association for online applications
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Incremental learning of gestures by imitation in a humanoid robot
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Hand gesture recognition based on dynamic Bayesian network framework
Pattern Recognition
Video Human Motion Recognition Using a Knowledge-Based Hybrid Method Based on a Hidden Markov Model
ACM Transactions on Intelligent Systems and Technology (TIST)
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Hand signals are commonly used in applications such as giving instructions to a pilot for airplane takeoff or direction of a crane operator by a foreman on the ground. A new algorithm for recognizing hand signals from a single camera is proposed. Typically, tracked 2D feature positions of hand signals are matched to 2D training images. In contrast, our approach matches the 2D feature positions to an archive of 3D motion capture sequences. The method avoids explicit reconstruction of the 3D articulated motion from 2D image features. Instead, the matching between the 2D and 3D sequence is done by backprojecting the 3D motion capture data onto 2D. Experiments demonstrate the effectiveness of the approach in an example application: recognizing six classes of basketball referee hand signals in video.