An Intelligent System for Recognizing the Human Activity using Improved Convolutional Neural Network (I-CNN) Model - Rao , A. Venugopal; Vishwakarma , Santosh Kumar; Kundu , Shakti (International Journal of Intelligent Systems and Applications in Engineering, 2023)
In account of the many practical uses of computer vision, researchers have made human behavior detection in videos a top priority. It covers a broad range of topics, such as video surveillance, behavior analysis, sports analysis, e-health, patient monitoring, assisted daily living, and much more. Many researchers have been proposed methods that rely on vision to identify human activity. Researchers will need to address problems of variations in human activity detection, class comparison between pictures, and temporal variation in order to construct an effective vision-based human behavior detection system. This paper proposes an improved Convolutional Neural Network (I-CNN) to boost human behavior detection performance and address these issues. A dilated convolution filter widens the convolutional layer’s receptive field to allow for the incorporation of more pertinent data. Additionally to the standard CNN model, it boosts CNN performance and reduces the calculation time. We’ve compiled KTH publicly available benchmark dataset, to test the effectiveness of suggested process. We show experimental findings showing that our suggested model outperforms the standard CNN model on the aforementioned dataset, achieving 96.85%.