UMAD: University of Macau Anomaly Detection Benchmark Dataset

• URL: http://arxiv.org/abs/2408.12527v1
• Date: Thu, 22 Aug 2024 16:32:19 GMT
• Title: UMAD: University of Macau Anomaly Detection Benchmark Dataset
• Authors: Dong Li, Lineng Chen, Cheng-Zhong Xu, Hui Kong,
• Abstract summary: We introduce the first benchmark dataset specifically for anomaly detection with reference in robotic patrolling scenarios. Our benchmark dataset is elaborated such that each query image can find a corresponding reference based on accurate robot localization. Besides the proposed benchmark dataset, we evaluate the baseline models of ADr on this dataset.
• Score: 26.25955201927986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Can I trust my anomaly detection system? A case study based on explainable AI [0.4416503115535552]
  This case study explores the robustness of an anomaly detection system based on variational autoencoder generative models. The goal is to get a different perspective on the real performances of anomaly detectors that use reconstruction differences.
  arXiv  Detail & Related papers  (2024-07-29T12:39:07Z)
• GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features [68.14842693208465]
  GeneralAD is an anomaly detection framework designed to operate in semantic, near-distribution, and industrial settings. We propose a novel self-supervised anomaly generation module that employs straightforward operations like noise addition and shuffling to patch features. We extensively evaluated our approach on ten datasets, achieving state-of-the-art results in six and on-par performance in the remaining.
  arXiv  Detail & Related papers  (2024-07-17T09:27:41Z)
• View-Invariant Pixelwise Anomaly Detection in Multi-object Scenes with Adaptive View Synthesis [0.0]
  Inspection and monitoring of infrastructure assets requires identifying visual anomalies in scenes periodically photographed over time. Images collected manually or with robots such as unmanned aerial vehicles from the same scene at different instances in time are typically not perfectly aligned. Current unsupervised pixel-level anomaly detection methods have mainly been developed for industrial settings. We present a novel network termed OmniAD to address the Scene AD problem posed.
  arXiv  Detail & Related papers  (2024-06-26T01:54:10Z)
• ARC: A Generalist Graph Anomaly Detector with In-Context Learning [62.202323209244]
  ARC is a generalist GAD approach that enables a one-for-all'' GAD model to detect anomalies across various graph datasets on-the-fly. equipped with in-context learning, ARC can directly extract dataset-specific patterns from the target dataset. Extensive experiments on multiple benchmark datasets from various domains demonstrate the superior anomaly detection performance, efficiency, and generalizability of ARC.
  arXiv  Detail & Related papers  (2024-05-27T02:42:33Z)
• A Hierarchically Feature Reconstructed Autoencoder for Unsupervised Anomaly Detection [8.512184778338806]
  It consists of a well pre-trained encoder to extract hierarchical feature representations and a decoder to reconstruct these intermediate features from the encoder. The anomalies can be detected when the decoder fails to reconstruct features well, and then errors of hierarchical feature reconstruction are aggregated into an anomaly map to achieve anomaly localization. Experiment results show that the proposed method outperforms the state-of-the-art methods on MNIST, Fashion-MNIST, CIFAR-10, and MVTec Anomaly Detection datasets.
  arXiv  Detail & Related papers  (2024-05-15T07:20:27Z)
• Are we certain it's anomalous? [57.729669157989235]
  Anomaly detection in time series is a complex task since anomalies are rare due to highly non-linear temporal correlations. Here we propose the novel use of Hyperbolic uncertainty for Anomaly Detection (HypAD) HypAD learns self-supervisedly to reconstruct the input signal.
  arXiv  Detail & Related papers  (2022-11-16T21:31:39Z)
• Self-Calibrating Anomaly and Change Detection for Autonomous Inspection Robots [0.07366405857677225]
  A visual anomaly or change detection algorithm identifies regions of an image that differ from a reference image or dataset. We propose a comprehensive deep learning framework for detecting anomalies and changes in a priori unknown environments.
  arXiv  Detail & Related papers  (2022-08-26T09:52:12Z)
• AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decoder [3.31490164885582]
  We propose a vision transformer-based encoder-decoder model, named AnoViT, to reflect normal information by additionally learning the global relationship between image patches. The proposed model performed better than the convolution-based model on three benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-21T09:01:37Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks [73.01104041298031]
  TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs) To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
  arXiv  Detail & Related papers  (2020-09-16T15:52:04Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.