Unsupervised Anomaly Detection from Time-of-Flight Depth Images

• URL: http://arxiv.org/abs/2203.01052v1
• Date: Wed, 2 Mar 2022 11:59:03 GMT
• Title: Unsupervised Anomaly Detection from Time-of-Flight Depth Images
• Authors: Pascal Schneider, Jason Rambach, Bruno Mirbach, Didier Stricker
• Abstract summary: Video anomaly detection (VAD) addresses the problem of automatically finding anomalous events in video data. We show that depth allows easy extraction of auxiliary information for scene analysis in the form of a foreground mask.
• Score: 11.485364355489462
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Video anomaly detection (VAD) addresses the problem of automatically finding anomalous events in video data. The primary data modalities on which current VAD systems work on are monochrome or RGB images. Using depth data in this context instead is still hardly explored in spite of depth images being a popular choice in many other computer vision research areas and the increasing availability of inexpensive depth camera hardware. We evaluate the application of existing autoencoder-based methods on depth video and propose how the advantages of using depth data can be leveraged by integration into the loss function. Training is done unsupervised using normal sequences without need for any additional annotations. We show that depth allows easy extraction of auxiliary information for scene analysis in the form of a foreground mask and demonstrate its beneficial effect on the anomaly detection performance through evaluation on a large public dataset, for which we are also the first ones to present results on.

Related papers

• Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts [57.01985221057047]
  This paper introduces a novel method that learnstemporal prompt embeddings for weakly supervised video anomaly detection and localization (WSVADL) based on pre-trained vision-language models (VLMs) Our method achieves state-of-theart performance on three public benchmarks for the WSVADL task.
  arXiv  Detail & Related papers  (2024-08-12T03:31:29Z)
• SelfReDepth: Self-Supervised Real-Time Depth Restoration for Consumer-Grade Sensors [42.48726526726542]
  SelfReDepth is a self-supervised deep learning technique for depth restoration. It uses multiple sequential depth frames and color data to achieve high-quality depth videos with temporal coherence. Our results demonstrate our approach's real-time performance on real-world datasets.
  arXiv  Detail & Related papers  (2024-06-05T15:38:02Z)
• Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encoder [1.4340883856076097]
  We propose Point Spatio-Temporal Auto-Encoder (PSTAE), an autoencoder framework that uses point cloud videos as input to detect anomalies in point cloud videos. Our method sets a new state-of-the-art (SOTA) on the TIMo dataset.
  arXiv  Detail & Related papers  (2023-06-04T10:30:28Z)
• Gated2Gated: Self-Supervised Depth Estimation from Gated Images [22.415893281441928]
  Gated cameras hold promise as an alternative to scanning LiDAR sensors with high-resolution 3D depth. We propose an entirely self-supervised depth estimation method that uses gated intensity profiles and temporal consistency as a training signal.
  arXiv  Detail & Related papers  (2021-12-04T19:47:38Z)
• Video Salient Object Detection via Contrastive Features and Attention Modules [106.33219760012048]
  We propose a network with attention modules to learn contrastive features for video salient object detection. A co-attention formulation is utilized to combine the low-level and high-level features. We show that the proposed method requires less computation, and performs favorably against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-11-03T17:40:32Z)
• Learning to Recover 3D Scene Shape from a Single Image [98.20106822614392]
  We propose a two-stage framework that first predicts depth up to an unknown scale and shift from a single monocular image. We then use 3D point cloud encoders to predict the missing depth shift and focal length that allow us to recover a realistic 3D scene shape.
  arXiv  Detail & Related papers  (2020-12-17T02:35:13Z)
• 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)
• Learning Monocular Dense Depth from Events [53.078665310545745]
  Event cameras produce brightness changes in the form of a stream of asynchronous events instead of intensity frames. Recent learning-based approaches have been applied to event-based data, such as monocular depth prediction. We propose a recurrent architecture to solve this task and show significant improvement over standard feed-forward methods.
  arXiv  Detail & Related papers  (2020-10-16T12:36:23Z)
• Deep Learning-based Single Image Face Depth Data Enhancement [15.41435352543715]
  This work proposes a deep learning face depth enhancement method in this context. Deep learning enhancers yield noticeably better results than the tested preexisting enhancers.
  arXiv  Detail & Related papers  (2020-06-19T11:52:38Z)
• 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)
• Single Image Depth Estimation Trained via Depth from Defocus Cues [105.67073923825842]
  Estimating depth from a single RGB image is a fundamental task in computer vision. In this work, we rely, instead of different views, on depth from focus cues. We present results that are on par with supervised methods on KITTI and Make3D datasets and outperform unsupervised learning approaches.
  arXiv  Detail & Related papers  (2020-01-14T20:22:54Z)

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.