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Full-Text Articles in Physical Sciences and Mathematics
Human Action Classification Based On Sequential Bag-Of-Words Model, Hong Liu, Qiaoduo Zhang, Qianru Sun
Human Action Classification Based On Sequential Bag-Of-Words Model, Hong Liu, Qiaoduo Zhang, Qianru Sun
Research Collection School Of Computing and Information Systems
Recently, approaches utilizing spatial-temporal features have achieved great success in human action classification. However, they typically rely on bag-of-words (BoWs) model, and ignore the spatial and temporal structure information of visual words, bringing ambiguities among similar actions. In this paper, we present a novel approach called sequential BoWs for efficient human action classification. It captures temporal sequential structure by segmenting the entire action into sub-actions. Each sub-action has a tiny movement within a narrow range of action. Then the sequential BoWs are created, in which each sub-action is assigned with a certain weight and salience to highlight the distinguishing sections. …
Action Classification By Exploring Directional Co-Occurrence Of Weighted Stips, Mengyuan Liu, Hong Liu, Qianru Sun
Action Classification By Exploring Directional Co-Occurrence Of Weighted Stips, Mengyuan Liu, Hong Liu, Qianru Sun
Research Collection School Of Computing and Information Systems
Human action recognition is challenging mainly due to intro-variety, inter-ambiguity and clutter backgrounds in real videos. Bag-of-visual words model utilizes spatio-temporal interest points(STIPs), and represents action by the distribution of points which ignores visual context among points. To add more contextual information, we propose a method by encoding spatio-temporal distribution of weighted pairwise points. First, STIPs are extracted from an action sequence and clustered into visual words. Then, each word is weighted in both temporal and spatial domains to capture the relationships with other words. Finally, the directional relationships between co-occurrence pairwise words are used to encode visual contexts. We …
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 …