Human posture classification using hybrid Particle Swarm Optimization

Accurate identification and categorization of different types of human postures is a vital element of a real time video-based surveillance system. In this paper, we present an approach known as the hybrid Particle Swarm Optimization (PSO+K) to classify human postures into their respective clusters. The PSO algorithm is used to search for possible optimal solution from the solution space. Then the results of the PSO are used as initial cluster centroids of the K-Means for further refinement to find the final optimal solution. Experimental results from the algorithm are compared with the K-Means and the conventional PSO algorithm using our posture dataset and the result shows that PSO+K produces better accuracies compared to other algorithms.