Optimization of particle swarm with fuzzy adaptive acceleration for human object detection

In this paper, a new approach to dynamically adapt the acceleration coefficient of particle swarm optimization (PSO) is proposed. The proposed method uses fuzzy inference system to lead the particles movement in exploring and exploiting the search area, therefore increases the accuracy and reduces the detection time of human object detection system. The performance of the proposed method is tested on real images, artificial images, and real-time video, the result is then compared to that of conventional method. Experiment on testing data using the proposed method improves the accuracy rate 9% better and almost twice faster than standard window scanning method. The proposed fast and accurate PSO with fuzzy adaptive acceleration gives a promising contribution to solve the real-world problem where computational time is critical.