Abstract
Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames, and existing large-scale VAD researches primarily focus on road traffic and human activity scenes. In industrial scenes, there are often a variety of unpredictable anomalies, and the VAD method can play a significant role in these scenarios. However, there is a lack of applicable datasets and methods specifically tailored for industrial production scenarios due to concerns regarding privacy and security. To bridge this gap, we propose a new dataset, IPAD, specifically designed for VAD in industrial scenarios. The industrial processes in our dataset are chosen through on-site factory research and discussions with engineers. This dataset covers 16 different industrial devices and contains over 6 hours of both synthetic and real-world video footage. Moreover, we annotate the key feature of the industrial process, ie, periodicity. Based on the proposed dataset, we introduce a period memory module and a sliding window inspection mechanism to effectively investigate the periodic information in a basic reconstruction model. Our framework leverages LoRA adapter to explore the effective migration of pretrained models, which are initially trained using synthetic data, into real-world scenarios. Our proposed dataset and method will fill the gap in the field of industrial video anomaly detection and drive the process of video understanding tasks as well as smart factory deployment.
Abstract (translated)
视频异常检测（VAD）是一个具有挑战性的任务，旨在识别视频帧中的异常情况，现有的大规模VAD研究主要集中在道路交通和人类活动场景。在工业场景中，通常存在多种不可预测的异常情况，VAD方法在这些场景中发挥着重要作用。然而，由于对隐私和安全问题的担忧，缺乏针对工业生产场景的可应用数据和方法。为了填补这一空白，我们提出了一个专门为工业场景设计的新的数据集IPAD。我们通过对现场工厂研究和与工程师的讨论来选择工业过程。这个数据集涵盖了16种不同的工业设备，包含了超过6小时的合成和现实世界的视频录像。此外，我们还对工业过程的关键特征，即周期性进行了标注。基于所提出的数据集，我们引入了周期记忆模块和滑动窗口检查机制，有效调查了基本重构模型的周期信息。我们的框架利用了LoRA适配器，探索将预训练模型有效迁移到真实世界场景。我们所提出的数据集和方法将填补工业视频异常检测领域中的空白，推动视频理解任务和智能工厂部署的发展。
URL
https://arxiv.org/abs/2404.15033
