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English(EN) Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection

新的扩散模型通过频率引导增强视频异常检测

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的用于骨骼视频异常检测的频率引导扩散模型。该模型通过训练一个生成器来产生扰动样本，从而增强了鲁棒性，这有助于重建模型更好地泛化到未见的正常运动。此外，它利用二维离散余弦变换来分离高频和低频运动分量，优先重建低频元素以实现更准确的异常检测。在五个数据集上的实验表明，该方法优于现有方法。 AI

影响 这项研究引入了一种新颖的视频异常检测方法，有望提高用于监控和监测系统的准确性和鲁棒性。

排序理由 详细介绍视频异常检测新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新的扩散模型通过频率引导增强视频异常检测

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详细介绍视频异常检测新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xiaofeng Tan, Hongsong Wang, Xin Geng, Liang Wang ·

    用于骨骼视频异常检测的带扰动训练的频率引导扩散模型

    arXiv:2412.03044v3 Announce Type: replace Abstract: Video anomaly detection (VAD) is a vital yet complex open-set task in computer vision, commonly tackled through reconstruction-based methods. However, these methods struggle with two key limitations: (1) insufficient robustness …

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