A hybrid elephant herding optimization and support vector machines for human behavior identification

Abstract

Human behavior identification has a great importance in daily life. Security, gaming, and medical diagnosis are vital applications in that field. This paper introduces a hybrid classification approach for human behavior identification employing support vector machines (SVMs) classifier hybrid with Elephant Herding Optimization algorithm (EHO). The Elephant Herding Optimization algorithm used to fine-tune SVM parameters and to select most discriminant features. Validation of the proposed approach will be accomplished using a computer vision-based data set named Vicon. It was acquired from multiple human action detection experiments. Results show superiority for the proposed approach over other techniques on the same data set regarding classification accuracy. EHO-SVM hybrid algorithm reaches 91.21 % and 90.62 % accuracies for two test cases with different action class selections.