Video Anomaly Detection via Prediction Network with Enhanced Spatio-Temporal Memory Exchange

• URL: http://arxiv.org/abs/2206.12914v1
• Date: Sun, 26 Jun 2022 16:10:56 GMT
• Title: Video Anomaly Detection via Prediction Network with Enhanced Spatio-Temporal Memory Exchange
• Authors: Guodong Shen, Yuqi Ouyang, Victor Sanchez
• Abstract summary: Video anomaly detection is a challenging task because most anomalies are scarce and non-deterministic. We design a Convolutional LSTM Auto-Encoder prediction framework with enhanced large-temporal memory exchange. Evaluations on three popular benchmarks show that our framework outperforms existing prediction-based anomaly detection methods.
• Score: 21.334952965297667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video anomaly detection is a challenging task because most anomalies are scarce and non-deterministic. Many approaches investigate the reconstruction difference between normal and abnormal patterns, but neglect that anomalies do not necessarily correspond to large reconstruction errors. To address this issue, we design a Convolutional LSTM Auto-Encoder prediction framework with enhanced spatio-temporal memory exchange using bi-directionalilty and a higher-order mechanism. The bi-directional structure promotes learning the temporal regularity through forward and backward predictions. The unique higher-order mechanism further strengthens spatial information interaction between the encoder and the decoder. Considering the limited receptive fields in Convolutional LSTMs, we also introduce an attention module to highlight informative features for prediction. Anomalies are eventually identified by comparing the frames with their corresponding predictions. Evaluations on three popular benchmarks show that our framework outperforms most existing prediction-based anomaly detection methods.

Related papers

• MIXAD: Memory-Induced Explainable Time Series Anomaly Detection [21.208134038200203]
  We introduce MIXAD (Memory Explainable Time Series Atemporally Detection), a model designed for interpretable anomaly detection. We also introduce a novel anomaly scoring method that detects significant shifts in memory activation patterns during anomalies. Our approach not only ensures decent detection performance but also outperforms state-of-the-art baselines by 34.30% and 34.51% in interpretability metrics.
  arXiv  Detail & Related papers  (2024-10-30T06:46:23Z)
• Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [59.34318192698142]
  We introduce a prior-less anomaly generation paradigm and develop an innovative unsupervised anomaly detection framework named GRAD. PatchDiff effectively expose various types of anomaly patterns. experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation.
  arXiv  Detail & Related papers  (2023-12-26T07:08:06Z)
• Multi-level Memory-augmented Appearance-Motion Correspondence Framework for Video Anomaly Detection [1.9511777443446219]
  We propose a multi-level memory-augmented appearance-motion correspondence framework. The latent correspondence between appearance and motion is explored via appearance-motion semantics alignment and semantics replacement training. Our framework outperforms the state-of-the-art methods, achieving AUCs of 99.6%, 93.8%, and 76.3% on UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets.
  arXiv  Detail & Related papers  (2023-03-09T08:43:06Z)
• Making Reconstruction-based Method Great Again for Video Anomaly Detection [64.19326819088563]
  Anomaly detection in videos is a significant yet challenging problem. Existing reconstruction-based methods rely on old-fashioned convolutional autoencoders. We propose a new autoencoder model for enhanced consecutive frame reconstruction.
  arXiv  Detail & Related papers  (2023-01-28T01:57:57Z)
• 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)
• Spatio-temporal predictive tasks for abnormal event detection in videos [60.02503434201552]
  We propose new constrained pretext tasks to learn object level normality patterns. Our approach consists in learning a mapping between down-scaled visual queries and their corresponding normal appearance and motion characteristics. Experiments on several benchmark datasets demonstrate the effectiveness of our approach to localize and track anomalies.
  arXiv  Detail & Related papers  (2022-10-27T19:45:12Z)
• Memory-augmented Adversarial Autoencoders for Multivariate Time-series Anomaly Detection with Deep Reconstruction and Prediction [4.033624665609417]
  We propose MemAAE, a novel unsupervised anomaly detection method for time-series. By jointly training two complementary proxy tasks, reconstruction and prediction, we show that detecting anomalies via multiple tasks obtains superior performance. MemAAE achieves an overall F1 score of 0.90 on four public datasets, significantly outperforming the best baseline by 0.02.
  arXiv  Detail & Related papers  (2021-10-15T18:29:05Z)
• Convolutional Transformer based Dual Discriminator Generative Adversarial Networks for Video Anomaly Detection [27.433162897608543]
  We propose Conversaal Transformer based Dual Discriminator Generative Adrial Networks (CT-D2GAN) to perform unsupervised video anomaly detection. It contains three key components, i., a convolutional encoder to capture the spatial information of input clips, a temporal self-attention module to encode the temporal dynamics and predict the future frame.
  arXiv  Detail & Related papers  (2021-07-29T03:07:25Z)
• FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation [6.112591965159383]
  We propose spatial rotation transformation (SRT) and temporal mixing transformation (TMT) to generate irregular patch cuboids within normal frame cuboids. Our model is evaluated on three anomaly detection benchmarks, achieving competitive accuracy and surpassing all the previous works in terms of speed.
  arXiv  Detail & Related papers  (2021-06-16T08:14:31Z)
• ESAD: End-to-end Deep Semi-supervised Anomaly Detection [85.81138474858197]
  We propose a new objective function that measures the KL-divergence between normal and anomalous data. The proposed method significantly outperforms several state-of-the-arts on multiple benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-09T08:16:35Z)
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders [51.691585766702744]
  We propose a variant of Adversarial Autoencoder which uses a mirrored Wasserstein loss in the discriminator to enforce better semantic-level reconstruction. We put forward an alternative measure of anomaly score to replace the reconstruction-based metric. Our method outperforms the current state-of-the-art methods for anomaly detection on several OOD detection benchmarks.
  arXiv  Detail & Related papers  (2020-03-24T08:26:58Z)

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.