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• W4313644212 endingPage "2716" @default.
• W4313644212 startingPage "2712" @default.
• W4313644212 abstract "Event cameras are novel bio-inspired sensors that offer advantages over traditional cameras (low latency, high dynamic range, low power, etc.). Optical flow estimation methods that work on packets of events trade off speed for accuracy, while event-by-event (incremental) methods have strong assumptions and have not been tested on common benchmarks that quantify progress in the field. Towards applications on resource-constrained devices, it is important to develop optical flow algorithms that are fast, light-weight and accurate. This work leverages insights from neuroscience, and proposes a novel optical flow estimation scheme based on triplet matching. The experiments on publicly available benchmarks demonstrate its capability to handle complex scenes with comparable results as prior packet-based algorithms. In addition, the proposed method achieves the fastest execution time ( <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$gt$</tex-math></inline-formula> 10 kHz) on standard CPUs as it requires only three events in estimation. We hope that our research opens the door to real-time, incremental motion estimation methods and applications in real-world scenarios." @default.
• W4313644212 created "2023-01-07" @default.
• W4313644212 creator A5034954593 @default.
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• W4313644212 date "2022-01-01" @default.
• W4313644212 modified "2023-09-30" @default.
• W4313644212 title "Fast Event-Based Optical Flow Estimation by Triplet Matching" @default.
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• W4313644212 doi "https://doi.org/10.1109/lsp.2023.3234800" @default.
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