End-to-end residual learning-based deep neural network model deployment for human activity recognition

Human activity recognition is a theme commonly explored in computer vision. Its applications in various domains include monitoring systems, video processing, robotics, and healthcare sector, etc. Activity recognition is a difficult task since there are structural changes among subjects, as well as inter-class and intra-class correlation between activities. As a result, a continuous intelligent control system for detecting human behavior with grouping of maximum information is necessary. Therefore, in this paper, a novel automatic system to identify human activity on the UTKinect dataset is implemented by using Residual learning-based Network “ResNet-50” and transfer learning to represent more complicated features and improved model robustness. The experimental results have shown an excellent generalization capability when tested on the validation set and obtained high accuracy of 98.60 per cent with a 0.02 loss score. The designed residual learning-based system indicates the efficiency of comparing with the other state-of-the-art models.