Related papers: Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate

Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate

URL: http://arxiv.org/abs/2010.07110v1
Date: Sat, 10 Oct 2020 04:46:16 GMT
Title: Online Anomaly Detection in Surveillance Videos with Asymptotic Bounds on False Alarm Rate
Authors: Keval Doshi, Yasin Yilmaz
Abstract summary: We propose an online anomaly detection method in surveillance videos with bounds on the false alarm rate. Our proposed algorithm consists of a multi-objective deep learning module along with a statistical anomaly detection module.
Score: 36.24563211765782
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Anomaly detection in surveillance videos is attracting an increasing amount of attention. Despite the competitive performance of recent methods, they lack theoretical performance analysis, particularly due to the complex deep neural network architectures used in decision making. Additionally, online decision making is an important but mostly neglected factor in this domain. Much of the existing methods that claim to be online, depend on batch or offline processing in practice. Motivated by these research gaps, we propose an online anomaly detection method in surveillance videos with asymptotic bounds on the false alarm rate, which in turn provides a clear procedure for selecting a proper decision threshold that satisfies the desired false alarm rate. Our proposed algorithm consists of a multi-objective deep learning module along with a statistical anomaly detection module, and its effectiveness is demonstrated on several publicly available data sets where we outperform the state-of-the-art algorithms. All codes are available at https://github.com/kevaldoshi17/Prediction-based-Video-Anomaly-Detection-.

Related papers

A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics [56.86150790999639]
We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer. The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
arXiv Detail & Related papers (2022-09-23T06:09:35Z)
A Critical Study on the Recent Deep Learning Based Semi-Supervised Video Anomaly Detection Methods [3.198144010381572]
This paper introduces the researchers of the field to a new perspective and reviews the recent deep-learning based semi-supervised video anomaly detection approaches. Our goal is to help researchers develop more effective video anomaly detection methods.
arXiv Detail & Related papers (2021-11-02T14:00:33Z)
A2Log: Attentive Augmented Log Anomaly Detection [53.06341151551106]
Anomaly detection becomes increasingly important for the dependability and serviceability of IT services. Existing unsupervised methods need anomaly examples to obtain a suitable decision boundary. We develop A2Log, which is an unsupervised anomaly detection method consisting of two steps: Anomaly scoring and anomaly decision.
arXiv Detail & Related papers (2021-09-20T13:40:21Z)
Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
arXiv Detail & Related papers (2021-01-28T16:38:26Z)
Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction [61.17654438176999]
We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design. Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
arXiv Detail & Related papers (2020-11-05T11:34:12Z)
Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining [2.0646127669654835]
We show how to use pre-trained convolutional neural net models to perform feature extraction and context mining. We derive contextual properties from the high-level features to further improve the performance of our video anomaly detection method.
arXiv Detail & Related papers (2020-10-06T00:26:14Z)
Anomaly Detection Under Controlled Sensing Using Actor-Critic Reinforcement Learning [31.841289319809807]
We consider the problem of detecting anomalies among a given set of processes using noisy binary sensor measurements. The noiseless sensor measurement corresponding to a normal process is 0, and the measurement is 1 if the process is anomalous. We propose a sequential sensor selection policy that dynamically determines which processes to observe at each time and when to terminate the detection algorithm.
arXiv Detail & Related papers (2020-05-26T22:53:17Z)
Any-Shot Sequential Anomaly Detection in Surveillance Videos [36.24563211765782]
We propose an online anomaly detection method for surveillance videos using transfer learning and any-shot learning. Our proposed algorithm leverages the feature extraction power of neural network-based models for transfer learning and the any-shot learning capability of statistical detection methods.
arXiv Detail & Related papers (2020-04-05T02:15:45Z)
Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection [114.9714355807607]
We show that applying self-trained deep ordinal regression to video anomaly detection overcomes two key limitations of existing methods. We devise an end-to-end trainable video anomaly detection approach that enables joint representation learning and anomaly scoring without manually labeled normal/abnormal data.
arXiv Detail & Related papers (2020-03-15T08:44:55Z)

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