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Full-Text Articles in Physical Sciences and Mathematics
Learning Spatio-Temporal Co-Occurrence Correlograms For Efficient Human Action Classification, Qianru Sun, Hong Liu
Learning Spatio-Temporal Co-Occurrence Correlograms For Efficient Human Action Classification, Qianru Sun, Hong Liu
Research Collection School Of Computing and Information Systems
Spatio-temporal interest point (STIP) based features show great promises in human action analysis with high efficiency and robustness. However, they typically focus on bag-of-visual words (BoVW), which omits any correlation among words and shows limited discrimination in real-world videos. In this paper, we propose a novel approach to add the spatio-temporal co-occurrence relationships of visual words to BoVW for a richer representation. Rather than assigning a particular scale on videos, we adopt the normalized google-like distance (NGLD) to measure the words' co-occurrence semantics, which grasps the videos' structure information in a statistical way. All pairwise distances in spatial and temporal …
Human Action Recognition Via Fused Kinematic Structure And Surface Representation, Salah R. Althloothi
Human Action Recognition Via Fused Kinematic Structure And Surface Representation, Salah R. Althloothi
Electronic Theses and Dissertations
Human action recognition from visual data has remained a challenging problem in the field of computer vision and pattern recognition. This dissertation introduces a new methodology for human action recognition using motion features extracted from kinematic structure, and shape features extracted from surface representation of human body. Motion features are used to provide sufficient information about human movement, whereas shape features are used to describe the structure of silhouette. These features are fused at the kernel level using Multikernel Learning (MKL) technique to enhance the overall performance of human action recognition. In fact, there are advantages in using multiple types …