A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation

• URL: http://arxiv.org/abs/2305.13611v1
• Date: Tue, 23 May 2023 02:20:12 GMT
• Title: A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation
• Authors: Congqi Cao, Yue Lu, Peng Wang and Yanning Zhang
• Abstract summary: We propose a new comprehensive dataset, NWPU Campus, containing 43 scenes, 28 classes of abnormal events, and 16 hours of videos. It is the largest semi-supervised VAD dataset with the largest number of scenes and classes of anomalies, the longest duration, and the only one considering the scene-dependent anomaly. We propose a novel model capable of detecting and anticipating anomalous events simultaneously.
• Score: 46.687762316415096
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Semi-supervised video anomaly detection (VAD) is a critical task in the intelligent surveillance system. However, an essential type of anomaly in VAD named scene-dependent anomaly has not received the attention of researchers. Moreover, there is no research investigating anomaly anticipation, a more significant task for preventing the occurrence of anomalous events. To this end, we propose a new comprehensive dataset, NWPU Campus, containing 43 scenes, 28 classes of abnormal events, and 16 hours of videos. At present, it is the largest semi-supervised VAD dataset with the largest number of scenes and classes of anomalies, the longest duration, and the only one considering the scene-dependent anomaly. Meanwhile, it is also the first dataset proposed for video anomaly anticipation. We further propose a novel model capable of detecting and anticipating anomalous events simultaneously. Compared with 7 outstanding VAD algorithms in recent years, our method can cope with scene-dependent anomaly detection and anomaly anticipation both well, achieving state-of-the-art performance on ShanghaiTech, CUHK Avenue, IITB Corridor and the newly proposed NWPU Campus datasets consistently. Our dataset and code is available at: https://campusvad.github.io.

Related papers

• Learn Suspected Anomalies from Event Prompts for Video Anomaly Detection [16.77262005540559]
  A novel framework is proposed to guide the learning of suspected anomalies from event prompts. It enables a new multi-prompt learning process to constrain the visual-semantic features across all videos. Our proposed model outperforms most state-of-the-art methods in terms of AP or AUC.
  arXiv  Detail & Related papers  (2024-03-02T10:42:47Z)
• 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)
• CHAD: Charlotte Anomaly Dataset [2.6774008509840996]
  We present the Charlotte Anomaly dataset (CHAD) for video anomaly detection. CHAD is the first anomaly dataset to include bounding box, identity, and pose annotations for each actor. With four camera views and over 1.15 million frames, CHAD is the largest fully annotated anomaly detection dataset.
  arXiv  Detail & Related papers  (2022-12-19T06:05:34Z)
• Are we certain it's anomalous? [57.729669157989235]
  Anomaly detection in time series is a complex task since anomalies are rare due to highly non-linear temporal correlations. Here we propose the novel use of Hyperbolic uncertainty for Anomaly Detection (HypAD) HypAD learns self-supervisedly to reconstruct the input signal.
  arXiv  Detail & Related papers  (2022-11-16T21:31:39Z)
• UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection [103.06327681038304]
  We propose a supervised open-set benchmark composed of multiple virtual scenes for video anomaly detection. Unlike existing data sets, we introduce abnormal events annotated at the pixel level at training time. We show that UBnormal can enhance the performance of a state-of-the-art anomaly detection framework.
  arXiv  Detail & Related papers  (2021-11-16T17:28:46Z)
• Anomaly Detection in Video Sequences: A Benchmark and Computational Model [25.25968958782081]
  We contribute a new Large-scale Anomaly Detection (LAD) database as the benchmark for anomaly detection in video sequences. It contains 2000 video sequences including normal and abnormal video clips with 14 anomaly categories including crash, fire, violence, etc. It provides the annotation data, including video-level labels (abnormal/normal video, anomaly type) and frame-level labels (abnormal/normal video frame) to facilitate anomaly detection. We propose a multi-task deep neural network to solve anomaly detection as a fully-supervised learning problem.
  arXiv  Detail & Related papers  (2021-06-16T06:34:38Z)
• Discriminative-Generative Dual Memory Video Anomaly Detection [81.09977516403411]
  Recently, people tried to use a few anomalies for video anomaly detection (VAD) instead of only normal data during the training process. We propose a DiscRiminative-gEnerative duAl Memory (DREAM) anomaly detection model to take advantage of a few anomalies and solve data imbalance.
  arXiv  Detail & Related papers  (2021-04-29T15:49:01Z)
• 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)
• 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)

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.