Monocular 3D Hand Tracking Using Particle Filter with Partitioned Sampling

2013 ◽  
Vol 10 (15) ◽  
pp. 4733-4742
Author(s):  
Bingchao Liu
Keyword(s):  
Particle Filter ◽  
Hand Tracking ◽  
Partitioned Sampling

Real-time hand tracking using a mean shift embedded particle filter

2007 ◽  
Vol 40 (7) ◽  
pp. 1958-1970 ◽  
Author(s):  
Caifeng Shan ◽  
Tieniu Tan ◽  
Yucheng Wei
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Mean Shift ◽  
Hand Tracking

Three dimensional hand tracking by improved particle swarm optimized particle filter

2014 ◽  
Vol 22 (10) ◽  
pp. 2870-2878
Author(s):  
李东年 LI Dong-nian ◽  
周以齐 ZHOU Yi-qi
Keyword(s):  
Particle Filter ◽  
Three Dimensional ◽  
Particle Swarm ◽  
Hand Tracking

scholarly journals Sequential particle filter with covariance features classified with artificial neural nets for continuous Indian sign language recognition

2017 ◽  
Vol 7 (1.1) ◽  
pp. 539
Author(s):  
P Praveen Kumar ◽  
P V.G.D. Prasad Reddy ◽  
P Srinivasa Rao
Keyword(s):  
Neural Network ◽  
Particle Filter ◽  
Sign Language ◽  
Covariance Matrix ◽  
Neural Nets ◽  
Hand Tracking ◽  
Compact Representation ◽  
Neural Network Classifier ◽  
Language Recognition ◽  
Sign Language Recognition

Machine translation of sign language is a complex and challenging problem in computer vision research. In this work, we propose to handle issues such as hands tracking, feature representation and classification for efficient interpretation of sign language from isolated sign videos. Hands tracking is attempted in a sequential format with one hand after the other by nullifying the effects of head movement using serial particle filter. The estimated hand positions in the video sequence are used to extract the hand portions to create a feature covariance matrix. This matrix is a compact representation of the hand features representing a sign. Adaptability of the feature covariance matrix is explored in developing relationships with new signs without creating a new feature matrix for individual signs. The extracted features are then applied to a neural network classifier which is trained with error backpropagation algorithm. Multiple experiments were conducted on a 181 class signs with 50 sentence formations with 5 different signers. Experimental results show the proposed sequential hand tracking is closer to ground truth. The proposed covariance features resulted in a classification accuracy of 89.34% with the neural network classifier.

2D Articulated Pose Tracking Using Particle Filter with Partitioned Sampling and Model Constraints

2009 ◽  
Vol 58 (2) ◽  
pp. 109-124 ◽  
Author(s):  
Chenguang Liu ◽  
Peng Liu ◽  
Jiafeng Liu ◽  
Jianhua Huang ◽  
Xianglong Tang
Keyword(s):  
Particle Filter ◽  
Pose Tracking ◽  
Partitioned Sampling
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