Evaluation of Local Space-time Descriptors based on Cuboid Detector in Human Action Recognition
HA Abdul-Azim; Magda B Fayek; Elsayed E Hemayed,;
Abstract
Human action recognition remains a challenging problem for researchers. Several action representation approaches have been proposed to improve the action recognition performance. Recently, local space-time features have become a popular representation approach for human actions in video sequences. Many different space-time detectors and descriptors have been proposed. They are evaluated on different datasets using different experimental conditions. In this paper, the performance of Cuboid detector is evaluated with four space-time description methods; namely, Gradient, HOG, HOF and HOG-HOF. All descriptors were tested on two datasets (KTH and Weizmann) using the bag-of-words model and Support Vector Machine.
Other data
Title | Evaluation of Local Space-time Descriptors based on Cuboid Detector in Human Action Recognition | Authors | HA Abdul-Azim ; Magda B Fayek ; Elsayed E Hemayed, | Keywords | Space-time features; Cuboid detector; space-time feature descriptors; bag-of-words; human action recognition | Issue Date | 11-Dec-2014 | Publisher | International Journal of Innovation and Applied Studies | Journal | International Journal of Innovation and Applied Studies |
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