Fast human detection using Gaussian Particle Swarm Optimization


Human detection is a challenging task in many fields because it is difficult to detect humans due to their varying appearance and posture. The evaluation speed of the method is important as well as its accuracy. In this paper, we propose a novel method using Gaussian Particle Swarm Optimization (Gaussian-PSO) for human detection with the Histograms of Oriented Gradients (HOG) feature to achieve a fast and accurate performance. Keeping the robustness of HOG feature on human detection, we raise the process speed in detection process so that it can be used for real-time applications. These advantages are given by a simple process which needs only one linear-SVM classifier with HOG features and Gaussian-PSO procedure.

In International Conference on Digital Ecosystems and Technologies (DEST), IEEE.