A Temporal Convolutional Neural Network Based Activity Recognition Model using a Real-Time Two-Dimensional Single Pose Estimation Framework

Human pose estimation and human activity recognition are two of the most researched domains in computer vision applications. Surveillance, human-computer interaction and video retrieval, all benefit from robust solutions of this domain. Due to disparities in inter-personal parameters, mobility efficiency, and recording configurations, the process is challenging. In this paper, deep learning algorithms are used for both pose estimation and activity recognition models. The pose estimation model generates an array of objects for each keypoint with their coordinated over a specific interval. This time series data is then consumed by the activity recognition model which was trained using a temporal convolutional neural network. The attained accuracy of the model was found to be 92.7% with a loss of 0.19.