Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions

• URL: http://arxiv.org/abs/2301.00114v4
• Date: Thu, 18 Jan 2024 03:20:19 GMT
• Title: Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions
• Authors: Pratik K. Mishra, Alex Mihailidis, Shehroz S. Khan
• Abstract summary: We present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. We conclude that skeleton-based approaches for anomaly detection can be a plausible privacy-protecting alternative for video anomaly detection.
• Score: 3.813649699234981
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground. Structural information in the form of skeletons describing the human motion in the videos is privacy-protecting and can overcome some of the problems posed by appearance-based features. In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. We present a novel taxonomy of algorithms based on the various learning approaches. We conclude that skeleton-based approaches for anomaly detection can be a plausible privacy-protecting alternative for video anomaly detection. Lastly, we identify major open research questions and provide guidelines to address them.

Related papers

• Learning Expressive And Generalizable Motion Features For Face Forgery Detection [52.54404879581527]
  We propose an effective sequence-based forgery detection framework based on an existing video classification method. To make the motion features more expressive for manipulation detection, we propose an alternative motion consistency block. We make a general video classification network achieve promising results on three popular face forgery datasets.
  arXiv  Detail & Related papers  (2024-03-08T09:25:48Z)
• Dynamic Erasing Network Based on Multi-Scale Temporal Features for Weakly Supervised Video Anomaly Detection [103.92970668001277]
  We propose a Dynamic Erasing Network (DE-Net) for weakly supervised video anomaly detection. We first propose a multi-scale temporal modeling module, capable of extracting features from segments of varying lengths. Then, we design a dynamic erasing strategy, which dynamically assesses the completeness of the detected anomalies.
  arXiv  Detail & Related papers  (2023-12-04T09:40:11Z)
• Understanding the Challenges and Opportunities of Pose-based Anomaly Detection [2.924868086534434]
  Pose-based anomaly detection is a video-analysis technique for detecting anomalous events or behaviors by examining human pose extracted from the video frames. In this work, we analyze and quantify the characteristics of two well-known video anomaly datasets to better understand the difficulties of pose-based anomaly detection. We believe these experiments are beneficial for a better comprehension of pose-based anomaly detection and the datasets currently available.
  arXiv  Detail & Related papers  (2023-03-09T18:09:45Z)
• Explainable Anomaly Detection in Images and Videos: A Survey [49.07140708026425]
  Anomaly detection and localization of visual data, including images and videos, are of great significance in 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.
  arXiv  Detail & Related papers  (2023-02-13T20:17:41Z)
• Audio-Visual Person-of-Interest DeepFake Detection [77.04789677645682]
  The aim of this work is to propose a deepfake detector that can cope with the wide variety of manipulation methods and scenarios encountered in the real world. We leverage a contrastive learning paradigm to learn the moving-face and audio segment embeddings that are most discriminative for each identity. Our method can detect both single-modality (audio-only, video-only) and multi-modality (audio-video) attacks, and is robust to low-quality or corrupted videos.
  arXiv  Detail & Related papers  (2022-04-06T20:51:40Z)
• 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)
• Deep Video Anomaly Detection: Opportunities and Challenges [12.077052764803161]
  Anomaly detection is a popular and vital task in various research contexts. Deep learning has shown its capacity in a number of domains, ranging from acoustics, images, to natural language processing. There are numerous advantages if such intelligent systems could be realised in our daily lives.
  arXiv  Detail & Related papers  (2021-10-11T08:41:51Z)
• Self-Supervised Representation Learning for Visual Anomaly Detection [9.642625267699488]
  We consider the problem of anomaly detection in images videos, and present a new visual anomaly detection technique for videos. We propose a simple self-supervision approach for learning temporal coherence across video frames without the use of any optical flow information. This intuitive approach shows superior performance of visual anomaly detection compared to numerous methods for images and videos on UCF101 and ILSVRC2015 video datasets.
  arXiv  Detail & Related papers  (2020-06-17T04:37:29Z)
• VideoForensicsHQ: Detecting High-quality Manipulated Face Videos [77.60295082172098]
  We show how the performance of forgery detectors depends on the presence of artefacts that the human eye can see. We introduce a new benchmark dataset for face video forgery detection, of unprecedented quality.
  arXiv  Detail & Related papers  (2020-05-20T21:17:43Z)
• 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.

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