Anomaly detection is critical in surveillance systems and patrol robots by identifying anomalous regions in images for early warning. Depending on whether reference data are utilized, anomaly detection can be categorized into anomaly detection with reference and anomaly detection without reference. Currently, anomaly detection without reference, which is closely related to out-of-distribution (OoD) object detection, struggles with learning anomalous patterns due to the difficulty of collecting sufficiently large and diverse anomaly datasets with the inherent rarity and novelty of anomalies. Alternatively, anomaly detection with reference employs the scheme of change detection to identify anomalies by comparing semantic changes between a reference image and a query one. However, there are very few ADr works due to the scarcity of public datasets in this domain. In this paper, we aim to address this gap by introducing the UMAD Benchmark Dataset. To our best knowledge, this is the first benchmark dataset designed specifically for anomaly detection with reference in robotic patrolling scenarios, e.g., where an autonomous robot is employed to detect anomalous objects by comparing a reference and a query video sequences. The reference sequences can be taken by the robot along a specified route when there are no anomalous objects in the scene. The query sequences are captured online by the robot when it is patrolling in the same scene following the same route. Our benchmark dataset is elaborated such that each query image can find a corresponding reference based on accurate robot localization along the same route in the prebuilt 3D map, with which the reference and query images can be geometrically aligned using adaptive warping. Besides the proposed benchmark dataset, we evaluate the baseline models of ADr on this dataset.

翻译：异常检测在监控系统和巡逻机器人中至关重要，它通过识别图像中的异常区域来实现早期预警。根据是否使用参考数据，异常检测可分为有参考异常检测和无参考异常检测。目前，无参考异常检测与分布外（OoD）目标检测密切相关，但由于异常本身具有罕见性和新颖性，难以收集足够大规模且多样化的异常数据集，导致学习异常模式面临挑战。另一种方式，有参考异常检测采用变化检测方案，通过比较参考图像与查询图像之间的语义变化来识别异常。然而，由于该领域公开数据集的稀缺，有参考异常检测的研究工作非常有限。本文旨在通过引入UMAD基准数据集来填补这一空白。据我们所知，这是首个专门为机器人巡逻场景中的有参考异常检测设计的基准数据集，例如，通过比较参考视频序列和查询视频序列，自主机器人用于检测异常物体。参考序列可由机器人在场景中无异常物体时沿指定路线采集。查询序列则由机器人在同一场景中沿相同路线巡逻时在线捕获。我们的基准数据集经过精心设计，使得每幅查询图像都能基于机器人在预建3D地图中沿相同路线的精确定位找到对应的参考图像，并可通过自适应扭曲实现参考图像与查询图像的几何对齐。除了提出的基准数据集，我们还在该数据集上评估了有参考异常检测的基线模型。