Towards Explainable Visual Anomaly Detection

Anomaly detection and localization of visual data, including images and videos, are of great significance in both machine learning academia and applied real-world scenarios. Despite the rapid development of visual anomaly detection techniques in recent years, the interpretations of these black-box models and reasonable explanations of why anomalies can be distinguished out are scarce. This paper provides the first survey concentrated on explainable visual anomaly detection methods. We first introduce the basic background of image-level anomaly detection and video-level anomaly detection, followed by the current explainable approaches for visual anomaly detection. Then, as the main content of this survey, a comprehensive and exhaustive literature review of explainable anomaly detection methods for both images and videos is presented. Finally, we discuss several promising future directions and open problems to explore on the explainability of visual anomaly detection

Anomaly Detection in Videos through Deep Unsupervised Techniques

Identifying abnormality in videos is an area of active research. Most of the work makes extensive use of supervised approaches, even though these methods often give superior performances the major drawback being abnormalities cannot be conformed to select classes, thus the need for unsupervised models to approach this task. We introduce Dirichlet Process Mixture Models (DPMM) along with Autoencoders to learn the normality in the data. Autoencoders have been extensively used in the literature for feature extraction and enable us to capture rich features into a small dimensional space. We use the Stick Breaking formulation of the DPMM which is a non-parametric version of the Gaussian mixture model and it can create new clusters as more and more data is observed. We exploit this property of the stick-breaking model to incorporate online learning and prediction of data in an unsupervised manner. We first introduce a two-phase model with feature extraction through autoencoders in the first step and then model inference through the DPMM in the second step. We seek to improve upon this model by introducing a model that does both the feature extraction and model inference in an end-to-end fashion by modeling the stick-breaking formulation to the Variational Autoencoder (VAE) setting

An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos

Videos represent the primary source of information for surveillance applications and are available in large amounts but in most cases contain little or no annotation for supervised learning. This article reviews the state-of-the-art deep learning based methods for video anomaly detection and categorizes them based on the type of model and criteria of detection. We also perform simple studies to understand the different approaches and provide the criteria of evaluation for spatio-temporal anomaly detection.Comment: 15 pages, double colum

Generative Neural Networks for Anomaly Detection in Crowded Scenes

International audienc