Reducing False Alarms in Video Surveillance by Deep Feature Statistical Modeling

• URL: http://arxiv.org/abs/2307.04159v1
• Date: Sun, 9 Jul 2023 12:37:17 GMT
• Title: Reducing False Alarms in Video Surveillance by Deep Feature Statistical Modeling
• Authors: Xavier Bou, Aitor Artola, Thibaud Ehret, Gabriele Facciolo, Jean-Michel Morel, Rafael Grompone von Gioi
• Abstract summary: We develop a method-a weakly supervised a-contrario validation process, based on high dimensional statistical modeling of deep features. Experimental results reveal that the proposed a-contrario validation is able to largely reduce the number of false alarms at both pixel and object levels.
• Score: 16.311150636417256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting relevant changes is a fundamental problem of video surveillance. Because of the high variability of data and the difficulty of properly annotating changes, unsupervised methods dominate the field. Arguably one of the most critical issues to make them practical is to reduce their false alarm rate. In this work, we develop a method-agnostic weakly supervised a-contrario validation process, based on high dimensional statistical modeling of deep features, to reduce the number of false alarms of any change detection algorithm. We also raise the insufficiency of the conventionally used pixel-wise evaluation, as it fails to precisely capture the performance needs of most real applications. For this reason, we complement pixel-wise metrics with object-wise metrics and evaluate the impact of our approach at both pixel and object levels, on six methods and several sequences from different datasets. Experimental results reveal that the proposed a-contrario validation is able to largely reduce the number of false alarms at both pixel and object levels.

Related papers

• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture [58.60915132222421]
  We introduce an approach that is both general and parameter-efficient for face forgery detection. We design a forgery-style mixture formulation that augments the diversity of forgery source domains. We show that the designed model achieves state-of-the-art generalizability with significantly reduced trainable parameters.
  arXiv  Detail & Related papers  (2024-08-23T01:53:36Z)
• Self-Supervised Graph Transformer for Deepfake Detection [1.8133635752982105]
  Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset. Deepfake detection system must remain impartial to forgery types, appearance, and quality for guaranteed generalizable detection performance. This study introduces a deepfake detection framework, leveraging a self-supervised pre-training model that delivers exceptional generalization ability.
  arXiv  Detail & Related papers  (2023-07-27T17:22:41Z)
• On the Effectiveness of Image Manipulation Detection in the Age of Social Media [9.227950734832447]
  manipulation detection algorithms often rely on the manipulated regions being sufficiently'' different from the rest of the non-tampered regions in the image. We present an in-depth analysis of deep learning-based and learning-free methods, assessing their performance on benchmark datasets. We propose a novel deep learning-based pre-processing technique that accentuates the anomalies present in manipulated regions.
  arXiv  Detail & Related papers  (2023-04-19T04:05:54Z)
• Fast and Accurate Object Detection on Asymmetrical Receptive Field [0.0]
  This article proposes methods for improving object detection accuracy from the perspective of changing receptive fields. The structure of the head part of YOLOv5 is modified by adding asymmetrical pooling layers. The performances of the new model in this article are compared with original YOLOv5 model and analyzed from several parameters.
  arXiv  Detail & Related papers  (2023-03-15T23:59:18Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• 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)
• Meta Adversarial Perturbations [66.43754467275967]
  We show the existence of a meta adversarial perturbation (MAP) MAP causes natural images to be misclassified with high probability after being updated through only a one-step gradient ascent update. We show that these perturbations are not only image-agnostic, but also model-agnostic, as a single perturbation generalizes well across unseen data points and different neural network architectures.
  arXiv  Detail & Related papers  (2021-11-19T16:01:45Z)
• 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)
• 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)
• A Video Analysis Method on Wanfang Dataset via Deep Neural Network [8.485930905198982]
  We describe the new function for real-time multi-object detection in sports competition and pedestrians flow detection in public based on deep learning. Based on the proposed algorithm, we adopt wanfang sports competition dataset as the main test dataset. Our work also can used for pedestrians flow detection and pedestrian alarm tasks.
  arXiv  Detail & Related papers  (2020-02-28T04:09:53Z)

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.