Tuan Hue Thi

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This paper presents a unified framework for human action classification and localization in video using structured learning of local space-time features. Each human action class is represented by a set of its own compact set of local patches. In our approach, we first use a discriminative hierarchical Bayesian classifier to select those space-time interest(More)
In this paper, we present a robust framework for action recognition in video, that is able to perform competitively against the state-of-the-art methods, yet does not rely on sophisticated background subtraction pre-process to remove background features. In particular, we extend the Implicit Shape Modeling (ISM) of [10] for object recognition to 3D to(More)
This paper presents a new technique for action recognition in video using human body part-based approach, combining both local feature description of each body part, and global graphical model structure of the human action. The human body is divided into elementary points from which a Decomposable Triangulated Graph will be built. The temporal variation of(More)
Vision-based surveillance and monitoring is a potential alternative for early detection of respiratory disease outbreaks in urban areas complementing molecular diagnostics and hospital and doctor visit-based alert systems. Visible actions representing typical flu-like symptoms include sneeze and cough that are associated with changing patterns of hand to(More)
We develop a robust technique to find similar matches of human actions in video. Given a query video, Motion History Images (MHI) are constructed for consecutive keyframes. This is followed by dividing the MHI into local Motion-Shape regions, which allows us to analyze the action as a set of sparse space-time patches in 3D. Inspired by the idea of(More)
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