Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
Photorealistic Scene Reconstruction by Voxel Coloring
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
A benchmark for 3D mesh segmentation
ACM SIGGRAPH 2009 papers
Artificial Intelligence in Medicine
Building sparse multiple-kernel SVM classifiers
IEEE Transactions on Neural Networks
Kinematic jump processes for monocular 3D human tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
VISSYM'04 Proceedings of the Sixth Joint Eurographics - IEEE TCVG conference on Visualization
Human action recognition based on skeleton splitting
Expert Systems with Applications: An International Journal
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This paper addresses a learning-based human action recognition system from multiple images based on integrating features of segmented 3D human body parts such as face, torso, and limbs. The innovation of our proposed 3D human action recognition system consists of three parts: (1) 3D reconstruction of the target object by tracking the position of a target object in a scene to voxelize the accurate 3D human model, (2) Human body model segmentation into several human body parts using ellipsoidal models in the space of second-order three dimensional diffusion tensor fields, and (3) Classification and recognition of human actions from features of the segmented human model using Multiple-Kernel based Support Vector Machine. Experimental results on a set of test volume data show that our proposed method is very efficient to visualize and recognize the human action using few parameters which are independent to partial occlusion, dimension, and viewpoint.