DESIGN AND IMPLEMENTATION OF MULTI-FEATURE FUSION KERNEL CORRELATION FILTERING ALGORITHM BASED ON HLS

Peiyu Cong, Min Xie*, Kaiming Yang, Xiaofeng Zhang, Hongyan Su, Xiongjun Fu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In order to balance the accuracy and real-time performance of the moving target tracking system, an optimized design and implementation method based on high-level synthesis (HLS) of multi-feature fusion with kernel correlation filtering algorithms on FPGA is designed. This design improves the KCF algorithm with LBP and HOG features, and proposes a new dimensionality reduction method for LBP, which enhances the real-time performance while maintaining effective extraction of target features. The algorithm is implemented with FPGA, and a well acceleration effect is obtained on the basis of high precision. In test, the frame rate reaches 35 frames per second. Finally, it is verified through simulation that this feature extraction method can be used to process various image data such as infrared detection and SAR radar imaging, and has a wide range of applications.

Original languageEnglish
Title of host publicationIET Conference Proceedings
PublisherInstitution of Engineering and Technology
Pages645-649
Number of pages5
Volume2020
Edition9
ISBN (Electronic)9781839535406
DOIs
Publication statusPublished - 2020
Event5th IET International Radar Conference, IET IRC 2020 - Virtual, Online
Duration: 4 Nov 20206 Nov 2020

Conference

Conference5th IET International Radar Conference, IET IRC 2020
CityVirtual, Online
Period4/11/206/11/20

Keywords

  • FPGA
  • KERNEL CORRELATION FILTER
  • MUTI-FEATURE FUSION
  • TARGET TRACKING

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