Human Posture Estimation and Sustainable Events Classification via Pseudo-2D Stick Model and K-ary Tree Hashing
| Autor: | Kibum Kim, Israr Akhtar, Ahmad Jalal |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
K-ary tree
Computer science Geography Planning and Development Hash function TJ807-830 02 engineering and technology 010501 environmental sciences Management Monitoring Policy and Law TD194-195 01 natural sciences Renewable energy sources Silhouette 0202 electrical engineering electronic engineering information engineering GE1-350 K-ary tree hashing Pose 0105 earth and related environmental sciences sustainable events classification Environmental effects of industries and plants Renewable Energy Sustainability and the Environment Event (computing) business.industry context-aware features human pose estimation Pattern recognition Environmental sciences Tree (data structure) Statistical classification pseudo 2D stick model ray optimization 020201 artificial intelligence & image processing Artificial intelligence business Smoothing |
| Zdroj: | Sustainability Volume 12 Issue 23 Sustainability, Vol 12, Iss 9814, p 9814 (2020) |
| ISSN: | 2071-1050 |
| DOI: | 10.3390/su12239814 |
| Popis: | This paper suggests that human pose estimation (HPE) and sustainable event classification (SEC) require an advanced human skeleton and context-aware features extraction approach along with machine learning classification methods to recognize daily events precisely. Over the last few decades, researchers have found new mechanisms to make HPE and SEC applicable in daily human life-log events such as sports, surveillance systems, human monitoring systems, and in the education sector. In this research article, we propose a novel HPE and SEC system for which we designed a pseudo-2D stick model. To extract full-body human silhouette features, we proposed various features such as energy, sine, distinct body parts movements, and a 3D Cartesian view of smoothing gradients features. Features extracted to represent human key posture points include rich 2D appearance, angular point, and multi-point autocorrelation. After the extraction of key points, we applied a hierarchical classification and optimization model via ray optimization and a K-ary tree hashing algorithm over a UCF50 dataset, an hmdb51 dataset, and an Olympic sports dataset. Human body key points detection accuracy for the UCF50 dataset was 80.9%, for the hmdb51 dataset it was 82.1%, and for the Olympic sports dataset it was 81.7%. Event classification for the UCF50 dataset was 90.48%, for the hmdb51 dataset it was 89.21%, and for the Olympic sports dataset it was 90.83%. These results indicate better performance for our approach compared to other state-of-the-art methods. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|