Improved single target identification tracking algorithm based on IPSO-BP neural network

Abstract

Abstract Driven by deep learning techniques in recent years, single target recognition and tracking techniques have developed significantly, but face challenges of real-time and accuracy. In this study, an improved IPSO-BP network is formed by optimizing three critical aspects of the IPSO algorithm: adjusting the inertia weight calculation formula, improving the learning factor, and creating a new iterative formula for particle updating, which in turn is combined with a BP neural network. After iterative training, this paper constructs a single target recognition tracking algorithm with higher efficiency. The Algorithm’s performance is comprehensively tested through experimental simulation in terms of real-time, accuracy and stability. The results show that the improved Algorithm can achieve a frame rate (FPS) of up to 31 in single target recognition and tracking. The IOU value is as high as about 83% in some tests. The tracking success rate in different scenarios averages approximately 98.50%, the position error is controlled within 0.7 m, and the speed error averages 2.75 m/s. This improved IPSO-BP neural network effectively solves the problems of the current technology in the areas of real-time and accuracy, showing high stability and accuracy.