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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The discovery of depth sensors has brought new opportunities in the Human Action Research by providing depth image data. Compared to the conventional RGB image data, the depth image data has additional benefits like color, illumination invariant, and provides clues about the shape of body. Inspired with these benefits, we present a new Human Action Recognition model from depth images. For a given action video, the consideration of an entire frames constitutes less detailed information about the shape and movements of body. Hence we have proposed a new method called Frame Sampling to reduce the frame count and chooses only key frames. After key frames extraction, they are processed through Depth Motion Map for action representation followed by Support Vector Machine for classification. The developed model is evaluated on a standard public dataset captured by depth cameras. The experimental results demonstrate the superior performance compared with state-of-art methods. Keyword: new dataset; artificial intelligence; sensor; natural language processing; fake detector; machine learning.

FRAME SAMPLING ASSISTED DEPTH MOTION MAPS FOR DEPTH BASED HUMAN ACTION RECOGNITION