<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

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

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Driven by deep learning techniques in recent years, single target recognition and tracking techniques have developed significantly, but face challenges of...

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

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 02 sty 2024

Przyjęty: 08 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0336

Słowa kluczowe
IPSO, BP neural network, Inertia weights, Learning factor, Identification tracking

© 2024 Ting Xie, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 02 sty 2024

Przyjęty: 08 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0336

Słowa kluczoweIPSO, BP neural network, Inertia weights, Learning factor, Identification tracking

© 2024 Ting Xie, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Słowa kluczowe
IPSO, BP neural network, Inertia weights, Learning factor, Identification tracking