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• W2023395556 abstract "In recent years, human action recognition has become a key issue in all intelligence visual surveillance systems, and it is also an important area of research in computer vision. In this paper, a novel approach is presented for human action recognition. Associated sparse geometric features are extracted based on the second generation Bandelet transformation. Aiming at the latest development of Bandelet transformation, the statistical features were proposed to represent each frame of a video and information from geometric flow to characterise the image context, especially for human body shapes and inner information. Features are selected with AdaBoost algorithm, and test experiments are carried on AdaBoost and Support Vector Machines (SVM). This method is easier to be implemented in the real world, without the complex probabilistic modelling of motion patterns and no need background subtraction. Experimental results demonstrate that the proposed approach can achieve satisfying performance on the Weizmann action dataset and KTH dataset, and exhibits considerable robustness to the body’s size change, because of its inherent strictness in describing human action with geometric flows." @default.
• W2023395556 created "2016-06-24" @default.
• W2023395556 creator A5033237609 @default.
• W2023395556 creator A5042508743 @default.
• W2023395556 date "2014-10-14" @default.
• W2023395556 modified "2023-09-25" @default.
• W2023395556 title "Human action recognition with sparse geometric features" @default.
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• W2023395556 doi "https://doi.org/10.1179/1743131x14y.0000000091" @default.
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