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, longterm 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.