Sani Salisu^1,2, A. S. A. Mohamed^1,*, M. H. Jaafar³, Ainun S. B. Pauzi¹, Hussain A. Younis^1,4

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2385-2400, 2023, DOI:10.32604/cmc.2023.035904

Abstract In this article, a comprehensive survey of deep learning-based (DL-based) human pose estimation (HPE) that can help researchers in the domain of computer vision is presented. HPE is among the fastest-growing research domains of computer vision and is used in solving several problems for human endeavours. After the detailed introduction, three different human body modes followed by the main stages of HPE and two pipelines of two-dimensional (2D) HPE are presented. The details of the four components of HPE are also presented. The keypoints output format of two popular 2D HPE datasets and the most cited DL-based HPE articles from… More >

Yazeed Yasin Ghadi¹, Israr Akhter², Suliman A. Alsuhibany³, Tamara al Shloul⁴, Ahmad Jalal², Kibum Kim^5,*

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1455-1471, 2022, DOI:10.32604/iasc.2022.025013

Abstract Event detection systems are mainly used to observe and monitor human behavior via red green blue (RGB) images and videos. Event detection using RGB images is one of the challenging tasks of the current era. Human detection, position and orientation of human body parts in RGB images is a critical phase for numerous systems models. In this research article, the detection of human body parts by extracting context-aware energy features for event recognition is described. For this, silhouette extraction, estimation of human body parts, and context-aware features are extracted. To optimize the context-intelligence vector, we applied an artificial intelligence-based self-organized… More >