English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

This paper attempts to present human action recognition through the combination of local and global feature descriptors values, which are extracted from RGB and Depth videos. A video sequence is represented as a collection of spatio and spatio-temporal features. However, the challenging problems exist in both local and global descriptors for classifying human actions. We proposed a novel combination of the two descriptor methods, 3D trajectory and motion boundary histogram for the local feature and global Gist feature descriptor for the global feature (3DTrMBGG). To solve the problems of the structural information among the local descriptors, and clutter background and occlusion among the global descriptor, the combination of the local and global features descriptor is used. In this paper, there are three novel combination steps of video descriptors. First, combines motion and 3D trajectory shape descriptors. Second, extract the structural information using global gist descriptor. Third, combines these two descriptor steps to get the 3DTrMBGG feature vector from spatio-temporal domains. The results of the 3DTrMBGG features are used along with the K-mean clustering and multi-class support vector machine classifier. Our new method on several video actions improves performance on actions even with low movement rate and outperforms the competing state-of-the-art -temporal feature-based human action recognition methods.

Local and Global Feature Descriptors Combination from RGB-Depth Videos for Human Action Recognition