Visual anomaly detection is essential and commonly used for many tasks in the
field of computer vision. Recent anomaly detection datasets mainly focus on
industrial automated inspection, medical image analysis and video surveillance.
In order to broaden the application and research of anomaly detection in
unmanned supermarkets and smart manufacturing, we introduce the supermarket
goods anomaly detection (GoodsAD) dataset. It contains 6124 high-resolution
images of 484 different appearance goods divided into 6 categories. Each
category contains several common different types of anomalies such as
deformation, surface damage and opened. Anomalies contain both texture changes
and structural changes. It follows the unsupervised setting and only normal
(defect-free) images are used for training. Pixel-precise ground truth regions
are provided for all anomalies. Moreover, we also conduct a thorough evaluation
of current state-of-the-art unsupervised anomaly detection methods. This
initial benchmark indicates that some methods which perform well on the
industrial anomaly detection dataset (e.g., MVTec AD), show poor performance on
our dataset. This is a comprehensive, multi-object dataset for supermarket
goods anomaly detection that focuses on real-world applications.

本研究介绍了超市商品异常检测 (GoodsAD) 数据集，该数据集包含 484 种不同出现方式的商品的 6124 张高分辨率图像，每个类别包含几种常见的异常类型，并对所有异常提供像素精确的地面真实区域，并对当前最先进的无监督异常检测方法进行了彻底的评估。

PKU-GoodsAD: 无监督异常检测和分割用的超市商品数据集

PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly  Detection and Segmentation

Pose-based anomaly detection is a video-analysis technique for detecting
anomalous events or behaviors by examining human pose extracted from the video
frames. Utilizing pose data alleviates privacy and ethical issues. Also,
computation-wise, the complexity of pose-based models is lower than pixel-based
approaches. However, it introduces more challenges, such as noisy skeleton
data, losing important pixel information, and not having enriched enough
features. These problems are exacerbated by a lack of anomaly detection
datasets that are good enough representatives of real-world scenarios. In this
work, we analyze and quantify the characteristics of two well-known video
anomaly datasets to better understand the difficulties of pose-based anomaly
detection. We take a step forward, exploring the discriminating power of pose
and trajectory for video anomaly detection and their effectiveness based on
context. We believe these experiments are beneficial for a better comprehension
of pose-based anomaly detection and the datasets currently available. This will
aid researchers in tackling the task of anomaly detection with a more lucid
perspective, accelerating the development of robust models with better
performance.

本研究对两个视频异常数据集进行了分析和定量化，以更好地理解基于姿势的异常检测的困难之处，并探索了基于姿势和轨迹的区别能力以及基于上下文的有效性，从而加速更好性能、更具鲁棒性的模型的开发。