论文标题
闪电快速视频通过对抗知识蒸馏检测
Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillation
论文作者
论文摘要
我们提出了一个非常快速的帧级模型，用于视频中的异常检测，该模型通过从多个高度准确的对象级教师模型中提取知识来检测异常。为了提高学生的忠诚度，我们通过共同采用标准和对抗性蒸馏来提炼教师的低分辨率异常图，为每个教师引入一个对抗性歧视者，以区分目标和产生的异常图。我们在三个基准（Avenue，Hanghaitech，UCSD PED2）上进行实验，表明我们的方法比最快的竞争方法快7倍以上，并且比以对象为中心的模型快28至62倍，同时获得了与最近方法的可比结果。我们的评估还表明，由于其先前闻所未闻的1480 fps速度，我们的模型在速度和准确性之间实现了最佳的权衡。此外，我们进行了一项全面的消融研究，以证明我们的建筑设计选择是合理的。我们的代码可免费获得：https：//github.com/ristea/fast-aed。
We propose a very fast frame-level model for anomaly detection in video, which learns to detect anomalies by distilling knowledge from multiple highly accurate object-level teacher models. To improve the fidelity of our student, we distill the low-resolution anomaly maps of the teachers by jointly applying standard and adversarial distillation, introducing an adversarial discriminator for each teacher to distinguish between target and generated anomaly maps. We conduct experiments on three benchmarks (Avenue, ShanghaiTech, UCSD Ped2), showing that our method is over 7 times faster than the fastest competing method, and between 28 and 62 times faster than object-centric models, while obtaining comparable results to recent methods. Our evaluation also indicates that our model achieves the best trade-off between speed and accuracy, due to its previously unheard-of speed of 1480 FPS. In addition, we carry out a comprehensive ablation study to justify our architectural design choices. Our code is freely available at: https://github.com/ristea/fast-aed.
