Human activity recognition is an attractive subject in machine vision which is applicable to intelligent living environments. Daily human activities consist of several actions, in which the boundaries of these actions are different among individuals. The existence of Kinect cameras and RGB-D images, which record joints data, high-resolution RGB and depth images, has improved human action recognition. In this research, actions are recognized by applying the action’s order in the weakly supervised and semi-supervised learning model and extracting the RGB-D data feature. Frank–Wolfe algorithm, which is a constrained convex optimization algorithm, and the pairwise Frank–Wolfe algorithm, which is a developed model of the Frank–Wolfe algorithm, are used as learning models. The evaluation of the proposed method was carried out on the Watchn- Patch database. The results show a good performance of the proposed method.
