Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction

• URL: http://arxiv.org/abs/2011.02763v2
• Date: Thu, 27 May 2021 05:53:57 GMT
• Title: Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction
• Authors: Xuanzhao Wang, Zhengping Che, Bo Jiang, Ning Xiao, Ke Yang, Jian Tang, Jieping Ye, Jingyu Wang, Qi Qi
• Abstract summary: 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.
• Score: 61.17654438176999
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video anomaly detection is commonly used in many applications such as security surveillance and is very challenging.A majority of recent video anomaly detection approaches utilize deep reconstruction models, but their performance is often suboptimal because of insufficient reconstruction error differences between normal and abnormal video frames in practice. Meanwhile, frame prediction-based anomaly detection methods have shown promising performance. In this paper, we propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design which is more in line with the characteristics of surveillance videos. The proposed method is equipped with a multi-path ConvGRU-based frame prediction network that can better handle semantically informative objects and areas of different scales and capture spatial-temporal dependencies in normal videos. A noise tolerance loss is introduced during training to mitigate the interference caused by background noise. Extensive experiments have been conducted on the CUHK Avenue, ShanghaiTech Campus, and UCSD Pedestrian datasets, and the results show that our proposed method outperforms existing state-of-the-art approaches. Remarkably, our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.

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)
• Video Dynamics Prior: An Internal Learning Approach for Robust Video Enhancements [83.5820690348833]
  We present a framework for low-level vision tasks that does not require any external training data corpus. Our approach learns neural modules by optimizing over a corrupted sequence, leveraging the weights of the coherence-temporal test and statistics internal statistics.
  arXiv  Detail & Related papers  (2023-12-13T01:57:11Z)
• 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)
• Open-Vocabulary Video Anomaly Detection [57.552523669351636]
  Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. Recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-13T02:54:17Z)
• Delving into CLIP latent space for Video Anomaly Recognition [24.37974279994544]
  We introduce the novel method AnomalyCLIP, the first to combine Large Language and Vision (LLV) models, such as CLIP. Our approach specifically involves manipulating the latent CLIP feature space to identify the normal event subspace. When anomalous frames are projected onto these directions, they exhibit a large feature magnitude if they belong to a particular class.
  arXiv  Detail & Related papers  (2023-10-04T14:01:55Z)
• Multi-Contextual Predictions with Vision Transformer for Video Anomaly Detection [22.098399083491937]
  understanding of thetemporal context of a video plays a vital role in anomaly detection. We design a transformer model with three different contextual prediction streams: masked, whole and partial. By learning to predict the missing frames of consecutive normal frames, our model can effectively learn various normality patterns in the video.
  arXiv  Detail & Related papers  (2022-06-17T05:54:31Z)
• Anomaly detection in surveillance videos using transformer based attention model [3.2968779106235586]
  This research suggests using a weakly supervised strategy to avoid annotating anomalous segments in training videos. The proposed framework is validated on real-world dataset i.e. ShanghaiTech Campus dataset.
  arXiv  Detail & Related papers  (2022-06-03T12:19:39Z)
• Weakly Supervised Video Anomaly Detection via Center-guided Discriminative Learning [25.787860059872106]
  Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration. We propose an anomaly detection framework, called Anomaly Regression Net (AR-Net), which only requires video-level labels in training stage. Our method yields a new state-of-the-art result for video anomaly detection on ShanghaiTech dataset.
  arXiv  Detail & Related papers  (2021-04-15T06:41:23Z)
• A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels [17.615297975503648]
  Alous event detection in surveillance videos is a challenging and practical research problem among image and video processing community. We propose a weakly supervised anomaly detection framework based on deep neural networks which is trained in a self-reasoning fashion using only video-level labels. The proposed framework has been evaluated on publicly available real-world anomaly detection datasets including UCF-crime, ShanghaiTech and Ped2.
  arXiv  Detail & Related papers  (2020-08-27T02:14:15Z)
• 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.