☆ 4.7 Article

Self-Supervised Attentive Generative Adversarial Networks for Video Anomaly Detection

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2023)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Computer Science, Artificial Intelligence

Anomaly Detection With Bidirectional Consistency in Videos

Zhiwen Fang et al.

Summary: The core component of most anomaly detectors is a self-supervised model that models patterns in training samples and detects unexpected patterns as anomalies in testing samples. To overcome the problem of normal patterns, a mutual supervision approach is proposed, which leverages complementary information from mutual learning. By introducing consistency loss and evaluation criteria, the model's generalization ability and accuracy in detecting anomalies are improved.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

PEPSI: Fast and Lightweight Network for Image Inpainting

Yong-Goo Shin et al.

Summary: The PEPSI network utilizes a parallel decoding path architecture to improve image inpainting performance through both a coarse-to-fine inpainting path and a refined feature reconstruction path. Additionally, Diet-PEPSI significantly reduces network parameters while maintaining performance.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Video Anomaly Detection with Sparse Coding Inspired Deep Neural Networks

Weixin Luo et al.

Summary: This paper introduces an anomaly detection method based on Deep Neural Networks inspired by sparse coding, with a focus on Temporally-coherent Sparse Coding (TSC) and Sequential Iterative Soft-Thresholding Algorithm (SIATA). The approach utilizes a stacked Recurrent Neural Networks (sRNN) architecture for sparse coefficient optimization and further enhances it with sRNN-AE to improve efficiency and accuracy in anomaly detection. Extensive experiments have shown that sRNN-AE outperforms existing methods, making it an effective tool for anomaly detection.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Deep End-to-End One-Class Classifier

Mohammad Sabokrou et al.

Summary: By using an adversarial training approach to detect out-of-distribution samples in an end-to-end trainable deep model, the proposed method successfully learns the underlying distribution of the target class and outperforms other approaches.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

Video Frame Prediction by Deep Multi-Branch Mask Network

Sen Li et al.

Summary: Future frame prediction in video is a crucial problem in computer vision, facing challenges in modeling complex scene evolution and temporal-spatial correlation. This paper proposes a deep multi-branch mask network (DMMNet) that successfully combines optical flow warping and RGB pixel synthesizing methods, offering a more flexible masking network for video frame prediction.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

Normality Learning in Multispace for Video Anomaly Detection

Yu Zhang et al.

Summary: This study proposes a semi-supervised video anomaly detection method based on generative adversarial network and frame prediction, learning normality in both the original image space and latent space, and detecting events deviating from normality as anomalies. Experimental results demonstrate the effectiveness of the proposed method.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Online anomaly detection in surveillance videos with asymptotic bound on false alarm rate

Keval Doshi et al.

Summary: Anomaly detection in surveillance videos has gained increasing attention, but existing methods lack theoretical performance analysis and consideration of online decision making. Researchers proposed an online anomaly detection method with asymptotic bounds on the false alarm rate and demonstrated its effectiveness on multiple datasets.

PATTERN RECOGNITION (2021)

添加到收藏夹

Article Automation & Control Systems

Trajectory-Based Scene Understanding Using Dirichlet Process Mixture Model

Kelathodi Kumaran Santhosh et al.

Summary: The paper proposes an unsupervised and nonparametric method to learn frequently used paths in road traffic, considering time dependencies of moving objects and using distance-based scene learning. This allows for quick learning of traffic scenes without manual intervention on road markings, providing a beneficial approach for designing traffic monitoring applications.

IEEE TRANSACTIONS ON CYBERNETICS (2021)

添加到收藏夹

Article Computer Science, Information Systems

Spatial-Temporal Cascade Autoencoder for Video Anomaly Detection in Crowded Scenes

Nanjun Li et al.

Summary: This paper proposes a cuboid-patch-based method for time-efficient anomaly detection and localization in video surveillance, using a spatial-temporal cascade autoencoder to make full use of spatial and temporal cues from video data. The method consists of two main stages defined by two neural networks: a spatial-temporal adversarial autoencoder (ST-AAE) and a spatial-temporal convolutional autoencoder (ST-CAE). Experimental results show that the framework outperforms other state-of-the-art works in this field.

IEEE TRANSACTIONS ON MULTIMEDIA (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Online Learning-Based Multi-Stage Complexity Control for Live Video Coding

Chao Huang et al.

Summary: The proposed online learning-based multi-stage complexity control method accurately manages encoding complexity and adapts to different video characteristics, while also optimizing the accuracy of the CU decision model to strike a balance between complexity control and rate distortion performance, outperforming state-of-the-art algorithms.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2021)

添加到收藏夹

Article Computer Science, Information Systems

Multi-Encoder Towards Effective Anomaly Detection in Videos

Zhiwen Fang et al.

Summary: This study introduces a novel Multi-Encoder Single-Decoder network to tackle the pattern bias problem in anomaly detection in videos. By encoding motion and content cues individually, it achieves end-to-end learning ability and real-time running speed.

IEEE TRANSACTIONS ON MULTIMEDIA (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Learning Causal Temporal Relation and Feature Discrimination for Anomaly Detection

Peng Wu et al.

Summary: This article proposes a method consisting of four modules to leverage the effect of causal temporal relations and feature discrimination for weakly supervised anomaly detection. These modules include the causal temporal relation module, classifier, compactness module, and dispersion module. Extensive experiments on three public benchmarks demonstrate the importance of causal temporal relations and feature discrimination for anomaly detection.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Localizing Anomalies From Weakly-Labeled Videos

Hui Lv et al.

Summary: This paper proposes a Weakly Supervised Anomaly Localization (WSAL) method that focuses on temporally localizing anomalous segments within anomalous videos, achieving state-of-the-art performance on the UCF-Crime and TAD datasets. The method utilizes a high-order context encoding model and an enhancement strategy to improve accuracy, and also introduces a new traffic anomaly (TAD) dataset to diversify anomaly detection benchmarks.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2021)

添加到收藏夹

Article Automation & Control Systems

Spatiotemporal Anomaly Detection Using Deep Learning for Real-Time Video Surveillance

Rashmika Nawaratne et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

添加到收藏夹

Article Computer Science, Information Systems

Video Anomaly Detection and Localization Based on an Adaptive Intra-Frame Classification Network

Ke Xu et al.

IEEE TRANSACTIONS ON MULTIMEDIA (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Integrating prediction and reconstruction for anomaly detection

Yao Tang et al.

PATTERN RECOGNITION LETTERS (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Recurrent Temporal Aggregation Framework for Deep Video Inpainting

Dahun Kim et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2020)

添加到收藏夹

Article Computer Science, Information Systems

Learning Normal Patterns via Adversarial Attention-Based Autoencoder for Abnormal Event Detection in Videos

Hao Song et al.

IEEE TRANSACTIONS ON MULTIMEDIA (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

Attention-Driven Loss for Anomaly Detection in Video Surveillance

Joey Tianyi Zhou et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Fast sparse coding networks for anomaly detection in videos

Peng Wu et al.

PATTERN RECOGNITION (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Variational Bayesian Learning of Generalized Dirichlet-Based Hidden Markov Models Applied to Unusual Events Detection

Elise Epaillard et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2019)

添加到收藏夹

Article Computer Science, Theory & Methods

AnomalyNet: An Anomaly Detection Network for Video Surveillance

Joey Tianyi Zhou et al.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2019)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

GODS: Generalized One-class Discriminative Subspaces for Anomaly Detection

Jue Wang et al.

2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) (2019)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy

Qing Yu et al.

2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) (2019)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Latent Space Autoregression for Novelty Detection

Davide Abati et al.

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)

添加到收藏夹

Proceedings Paper Computer Science, Interdisciplinary Applications

AnoPCN: Video Anomaly Detection via Deep Predictive Coding Network

Muchao Ye et al.

PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA (MM'19) (2019)

添加到收藏夹

Article Computer Science, Information Systems

Anomaly detection in crowded scenes using motion energy model

Tianyu Chen et al.

MULTIMEDIA TOOLS AND APPLICATIONS (2018)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Perceptual Adversarial Networks for Image-to-Image Transformation

Chaoyue Wang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2018)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Adversarially Learned One-Class Classifier for Novelty Detection

Mohammad Sabokrou et al.

2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (2018)

添加到收藏夹

Article Engineering, Electrical & Electronic

Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos

Rensso Victor Hugo Mora Colque et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2017)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Video Anomaly Detection With Compact Feature Sets for Online Performance

Roberto Leyva et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2017)

添加到收藏夹

Article Engineering, Civil

Anomaly Detection in Traffic Scenes via Spatial-Aware Motion Reconstruction

Yuan Yuan et al.

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2017)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Colorization as a Proxy Task for Visual Understanding

Gustav Larsson et al.

30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017) (2017)

添加到收藏夹

Proceedings Paper Computer Science, Artificial Intelligence

Unmasking the abnormal events in video

Radu Tudor Ionescu et al.

2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)

添加到收藏夹

Article Engineering, Electrical & Electronic

Adaptive Sparse Representations for Video Anomaly Detection

Xuan Mo et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY (2014)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Anomaly Detection and Localization in Crowded Scenes

Weixin Li et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2014)

添加到收藏夹

Letter Computer Science, Artificial Intelligence

A Dynamic Hierarchical Clustering Method for Trajectory-Based Unusual Video Event Detection

Fan Jiang et al.

IEEE TRANSACTIONS ON IMAGE PROCESSING (2009)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Robust real-time unusual event detection using multiple fixed-location monitors

Amit Adam et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2008)

添加到收藏夹

Article Computer Science, Artificial Intelligence

A system for learning statistical motion patterns

Weiming Hu et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2006)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.