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- W4367016322 abstract "Most existing high-speed trackers are based on generative models, which cannot distinguish between target and background. In contrast, the discriminative correlation filter (DCF) based on discriminative model treats tracking as a classification of background and target. To address the problem that DCF cannot track at high speed. In this paper, we propose a high-speed GPU-based parallel discriminative correlation filter (PDCF), decompose the DCF into parallel tasks and map them to GPU with different coarse-grained and multi-level memory. The experiments result show that PDCF is 58 times faster than DCF running on a single CPU with the same accuracy, and the speed is 2213 frames per second (FPS) when the patch size is 200*200pixel, which fully satisfies the task of high-speed tracking." @default.
- W4367016322 created "2023-04-27" @default.
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- W4367016322 date "2023-02-24" @default.
- W4367016322 modified "2023-09-30" @default.
- W4367016322 title "High-Speed Tracking of Discriminative Correlation Filters Based on GPU" @default.
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- W4367016322 doi "https://doi.org/10.1109/nnice58320.2023.10105783" @default.
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