Development of New Models for Vision-Based Human Activity Recognition

Identifier:  TDX:3388

Handle: https://hdl.handle.net/20.500.11797/TDX3388

Authors:  Saleh Alraimi, Adel

Abstract:
Action recognition methods enable intelligent systems to recognize human actions in daily life videos. However, many action recognition methods give noticeable misclassification rates due to the big variations within the videos of the same class, and the changes in viewpoint, scale and background. To reduce the misclassification rate, we propose a new video representation method that captures the temporal evolution of the action happening in the whole video, a new method for human hands segmentation and a new method for human activity recognition in still images.