A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization With Gaussian Mutation

Human activity discovery aims to cluster human activities without any prior knowledge of what defines each activity. However, most existing methods for human activity recognition are supervised, relying on labeled inputs for training. In reality, it is challenging to label human activity data due to its large volume and the diversity of human activities. To address this issue, this article proposes an unsupervised framework for human activity discovery in 3-D skeleton sequences. The framework includes a data preprocessing step that selects important frames based on kinetic energy and extracts relevant features, such as joint displacement, statistical displacement, angles, and orientation. To reduce the dimensionality of the extracted features, the framework uses principle component analysis. Unlike many other methods for human activity discovery, the proposed framework is fully unsupervised and does not rely on presegmented videos. To segment the time series of activities, the framework uses a sliding time window with some overlapping. The hybrid particle swarm optimization (PSO) with Gaussian mutation and K-means algorithm is then proposed to discover the activities. PSO is chosen for its powerful global search capability and simple implementation. To further improve the convergence rate of PSO, K-means is applied to the outcome centroids from each iteration of PSO. The experimental results on five datasets demonstrate that the proposed framework has superior performance in discovering activities compared to other state-of-the-art methods. The framework achieves an average increase in accuracy of at least 4%.