An integrated human computer interaction scheme for object detection using deep learning. (December 2021)

• Record Type: Journal Article
• Authors: Saad, Aldosary; Mohamed, Abdallah A.
• Is Part Of: Computers & electrical engineering. Volume 96:Part A(2021)
• Publication Date: 2021-12
• Subjects: Computer vision -- Deep learning -- HCI -- Object detection -- Pre-classification; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107475
• Languages: English
• ISSNs: 0045-7906

Abstract

Research highlights: An integrated detection-based interaction scheme (IDIS) has been proposed for improving the accuracy and reliability of HCI systems. The recurrent process is used to identify the variations in interaction patterns and time. The proposed scheme's performance is verified using dataset sources for the following metrics: accuracy, delay, error, and interaction span. The classified input is verified using matching patterns throughout the interaction span. The pre-classification instances are verified using a DBN through training based on previous instances. Abstract: Human-computer interaction (HCI) and computer vision (CV) provide interesting communication features between machines and humans in different real-time applications. Visualization helps to improve the accuracy of detecting target communication objects for better interaction. This paper introduces an integrated detection-based interaction scheme (IDIS) for improving the accuracy and reliability of HCI systems. The input is fetched from the ranged object, and the interaction session is initiated after the classification and detection of the object. The process of robust, long-term interaction with the detected object is achieved through pre-classification. In this detection process, deep learning is used to identify the object and perform its intended interaction requirements. The recurrent process is used to identify the variations in interaction patterns and time. The allocation of interaction sessions is streamlined to improve the interaction span and detection accuracy. The proposed scheme's performance is verified using dataset sources for the following metrics: accuracy, delay, error, and interaction span. Graphical abstract: Integrated Detection-Based Interaction Scheme for Human-Computer Interaction through Reliable Detection Image, graphical abstract Figure: Integrated Detection-Based Interaction Scheme