A Modular and Unified Framework for Detecting and Localizing Video Anomalies

• URL: http://arxiv.org/abs/2103.11299v1
• Date: Sun, 21 Mar 2021 04:16:51 GMT
• Title: A Modular and Unified Framework for Detecting and Localizing Video Anomalies
• Authors: Keval Doshi and Yasin Yilmaz
• Abstract summary: We propose a modular and unified approach to the online video anomaly detection and localization problem, called MOVAD. It consists of a novel transfer learning based plug-and-play architecture, a sequential anomaly detector, a mathematical framework for selecting the detection threshold, and a suitable performance metric for real-time anomalous event detection in videos.
• Score: 30.83924581439373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly detection in videos has been attracting an increasing amount of attention. Despite the competitive performance of recent methods on benchmark datasets, they typically lack desirable features such as modularity, cross-domain adaptivity, interpretability, and real-time anomalous event detection. Furthermore, current state-of-the-art approaches are evaluated using the standard instance-based detection metric by considering video frames as independent instances, which is not ideal for video anomaly detection. Motivated by these research gaps, we propose a modular and unified approach to the online video anomaly detection and localization problem, called MOVAD, which consists of a novel transfer learning based plug-and-play architecture, a sequential anomaly detector, a mathematical framework for selecting the detection threshold, and a suitable performance metric for real-time anomalous event detection in videos. Extensive performance evaluations on benchmark datasets show that the proposed framework significantly outperforms the current state-of-the-art approaches.

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)
• VANE-Bench: Video Anomaly Evaluation Benchmark for Conversational LMMs [64.60035916955837]
  VANE-Bench is a benchmark designed to assess the proficiency of Video-LMMs in detecting anomalies and inconsistencies in videos. Our dataset comprises an array of videos synthetically generated using existing state-of-the-art text-to-video generation models. We evaluate nine existing Video-LMMs, both open and closed sources, on this benchmarking task and find that most of the models encounter difficulties in effectively identifying the subtle anomalies.
  arXiv  Detail & Related papers  (2024-06-14T17:59:01Z)
• 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)
• A Lightweight Video Anomaly Detection Model with Weak Supervision and Adaptive Instance Selection [14.089888316857426]
  This paper focuses on weakly supervised video anomaly detection. We develop a lightweight video anomaly detection model. We show that our model can achieve comparable or even superior AUC score compared to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-10-09T01:23:08Z)
• 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)
• A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video [120.18562044084678]
  Abnormal event detection in video is a complex computer vision problem that has attracted significant attention in recent years. We propose a background-agnostic framework that learns from training videos containing only normal events.
  arXiv  Detail & Related papers  (2020-08-27T18:39:24Z)
• 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)
• Localizing Anomalies from Weakly-Labeled Videos [45.58643708315132]
  We propose a WeaklySupervised Anomaly localization (WSAL) method focusing on temporally localizing anomalous segments within anomalous videos. Inspired by the appearance difference in anomalous videos, the evolution of adjacent temporal segments is evaluated for the localization of anomalous segments. Our proposed method achieves new state-of-the-art performance on the UCF-Crime and TAD datasets.
  arXiv  Detail & Related papers  (2020-08-20T12:58:03Z)

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.