A Hierarchical Framework for Video-Based Human Activity Recognition Using Body Part Interactions

Human Activity Recognition (HAR) is an important field with diverse applications. However, video-based HAR is challenging because of various factors, such as noise, multiple people, and obscured body parts. Moreover, it is difficult to identify similar activities within and across classes. This study presents a novel approach that utilizes body region relationships as features and a two-level hierarchical model for classification to address these challenges. The proposed system uses a Hidden Markov Model (HMM) at the first level to model human activity, and similar activities are then grouped and classified using a Support Vector Machine (SVM) at the second level. The performance of the proposed system was evaluated on four datasets, with superior results observed for the KTH and Basic Kitchen Activity (BKA) datasets. Promising results were obtained for the HMDB-51 and UCF101 datasets. Improvements of 25%, 25%, 4%, 22%, 24%, and 30% in accuracy, recall, specificity, Precision, F1-score, and MCC, respectively, are achieved for the KTH dataset. On the BKA dataset, the second level of the system shows improvements of 8.6%, 8.6%, 0.85%, 8.2%, 8.4%, and 9.5% for the same metrics compared to the first level. These findings demonstrate the potential of the proposed two-level hierarchical system for human activity recognition applications.