A probability-dynamic Particle Swarm Optimization for object tracking

Abstract

Particle Swarm Optimization has been used in many research and application domain popularly since its development and improvement. Due to its fast and accurate solution searching, PSO has become one of the high potential tools to provide better outcomes to solve many practical problems. In image processing and object tracking applications, PSO also indicates to have good performance in both linear and non-linear object moving pattern, many scientists conduct development and research to implement not only basic PSO but also improved methods in enhancing the efficiency of the algorithm to achieve precise object tracking orbit. This paper is aim to propose a new improved PSO by comparing the inertia weight and constriction factor of PSO. It provides faster and more accurate object tracking process since the proposed algorithm can inherit some useful information from the previous solution to perform the dynamic particle movement when other better solution exists. The testing experiments have been done for different types of video, results showed that the proposed algorithm can have better quality of tracking performance and faster object retrieval speed. The proposed approach has been developed in C++ environment and tested against videos and objects with multiple moving patterns to demonstrate the benefits with precise object similarity.