Vesal Khean¹, Chomyong Kim², Sunjoo Ryu², Awais Khan¹, Min Kyung Hong³, Eun Young Kim⁴, Joungmin Kim⁵, Yunyoung Nam^3,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 773-787, 2024, DOI:10.32604/cmc.2024.056767 - 15 October 2024

Abstract Human Interaction Recognition (HIR) was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements. HIR requires more sophisticated analysis than Human Action Recognition (HAR) since HAR focuses solely on individual activities like walking or running, while HIR involves the interactions between people. This research aims to develop a robust system for recognizing five common human interactions, such as hugging, kicking, pushing, pointing, and no interaction, from video sequences using multiple cameras. In this study, a hybrid Deep… More >

Tanvir Fatima Naik Bukht¹, Naif Al Mudawi², Saud S. Alotaibi³, Abdulwahab Alazeb², Mohammed Alonazi⁴, Aisha Ahmed AlArfaj⁵, Ahmad Jalal¹, Jaekwang Kim^6,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1557-1573, 2023, DOI:10.32604/cmc.2023.041335 - 29 November 2023

Abstract Human-human interaction recognition is crucial in computer vision fields like surveillance, human-computer interaction, and social robotics. It enhances systems’ ability to interpret and respond to human behavior precisely. This research focuses on recognizing human interaction behaviors using a static image, which is challenging due to the complexity of diverse actions. The overall purpose of this study is to develop a robust and accurate system for human interaction recognition. This research presents a novel image-based human interaction recognition method using a Hidden Markov Model (HMM). The technique employs hue, saturation, and intensity (HSI) color transformation to… More >