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- W2892424932 abstract "Detecting abnormal activities is a crucial research topic nowadays because of its wide variety of applications in such as security monitoring, video surveillance and healthcare applications. The proposed method is used to distinguish between normal and abnormal human activities. Two-dimensional visual saliency map is created from color video sequences and used for further processing. Selective spatio-temporal interest point (STIP) detector is used to extract interest point features from saliency. 3D Image gradients are calculated using intensity patches to describe STIPs and feature vector is computed by quantizing them. The activities are described finally using bag-of-features representation. Support Vector Machine is used as a classifier to distinguish between normal and abnormal activities. The performance of the system is evaluated using UR fall Dataset and dataset S provided by Le2i CNRS that shows significant accuracy." @default.
- W2892424932 created "2018-10-05" @default.
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- W2892424932 date "2018-01-01" @default.
- W2892424932 modified "2023-09-27" @default.
- W2892424932 title "Abnormal Activity Recognition Using Saliency and Spatio-Temporal Interest Point Detector" @default.
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- W2892424932 doi "https://doi.org/10.1007/978-981-13-1936-5_32" @default.
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