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Design of Dynamic Target Tracking System Based on FPGADynamic Target Tracking System

Published: 01 June 2024 Publication History

Abstract

With the development of the electronic information industry, image processing technology has become increasingly important in the field of computer vision, and has been widely applied in the field of motion target detection and tracking. This article proposes a design scheme for a motion target detection and tracking system based on FPGA platform. The camera collects image information, detects the motion target through grayscale transformation, inter frame difference algorithm, and performs image preprocessing operations such as median filtering. Then, the bounding box technology is used to determine the position of the motion target, calculate the center coordinate, and finally control the 2-DOF pan-tilt Platform to rotate the camera to achieve target tracking. After experimental testing, the system can accurately detect dynamic targets and track them in real-time by rotating the camera. This system will be widely applied in fields such as intelligent monitoring and autonomous driving.

References

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Imane El Manaa, My Abdelouahed Sabri, Abdellah Aarab. 2019. Classification and segmentation approach for detecting moving object in road applications. IEEE, 1-5. https://doi.org/10.1109/ISACS48493. 2019. 9068902.
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T. Sledeviè, A. Serackis and D. Plonis. 2018.FPGA-Based Selected Object Tracking Using LBP, HOG and Motion Detection.In 2018 IEEE 6th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE). IEEE, 1-5. https://doi.org/10.1109/AIEEE. 2018. 8592410.
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Chenwei Dong, Junlei Zhou, Weipeng Wen, Si Chen. 2022. Deep Learning Based Multi-Target Multi-Camera Tracking System. In Proceedings of the 8th International Conference on Computing and Artificial Intelligence. ACM, 419–424. https://doi.org/10.1145/3532213.3532276.
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  1. Design of Dynamic Target Tracking System Based on FPGADynamic Target Tracking System

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    AISNS '23: Proceedings of the 2023 International Conference on Artificial Intelligence, Systems and Network Security
    December 2023
    467 pages
    ISBN:9798400716966
    DOI:10.1145/3661638
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 01 June 2024

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