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Anomaly Detection in Traffic Scenes via Spatial-Aware Motion Reconstruction

Yuan Yuan, Dong Wang, Qi Wang
2017 IEEE transactions on intelligent transportation systems (Print)  
To tackle these specific problems, this paper proposes a spatial localization constrained sparse coding approach for anomaly detection in traffic scenes, which firstly measures the abnormality of motion  ...  coding methods. 3) Results of motion orientation and magnitude are adaptively weighted and fused by a Bayesian model, which makes the proposed method more robust and handle more kinds of abnormal events  ...  OVERVIEW In this paper, an effective anomaly detection method for traffic scenes is designed, which is robust to the change of the camera movement.  ... 
doi:10.1109/tits.2016.2601655 fatcat:ep3osyr3zjgllnfu5j6fi34scq

Anomaly Detection via Graphical Lasso [article]

Haitao Liu, Randy C. Paffenroth, Jian Zou, Chong Zhou
2018 arXiv   pre-print
Nonetheless, a precise definition of anomalies is important for automated detection and herein we approach such problems from the perspective of detecting sparse latent effects embedded in large collections  ...  Accordingly, anomaly detection is important both for analyzing the anomalies themselves and for cleaning the data for further analysis of its ambient structure.  ...  Conclusion and Future Work In this paper, we propose a Robust Graphical Lasso (Rglasso) to detect sparse latent effects via Graphical Lasso.  ... 
arXiv:1811.04277v1 fatcat:na64xwv6pfbwdg64atjc7b3rrm

An Improved Robust Principal Component Analysis Model for Anomalies Detection of Subway Passenger Flow

Xuehui Wang, Yong Zhang, Hao Liu, Yang Wang, Lichun Wang, Baocai Yin
2018 Journal of Advanced Transportation  
In this paper, we propose a novel anomalies detection method of subway passenger flow.  ...  In this method, an improved robust principal component analysis model is presented to detect anomalies; then ST-DBSCAN algorithm is used to group the station-level anomaly data on space-time dimensions  ...  Anomalies Detection Methods of Passenger Flow.  ... 
doi:10.1155/2018/7191549 fatcat:rob45qotgvbxthm63mg5rmgpfi

Quantifying Urban Traffic Anomalies [article]

Zhengyi Zhou , Chris Volinsky
2016 arXiv   pre-print
First, we adapt stable principal component pursuit to detect anomalies for each road segment. This allows us to pinpoint traffic anomalies early and precisely in space.  ...  Detecting and quantifying anomalies in urban traffic is critical for real-time alerting or re-routing in the short run and urban planning in the long run.  ...  Related work Many traffic anomaly detection methods are region-based, discretizing a city into several regions, and anomaly detection is done across regions.  ... 
arXiv:1610.00579v1 fatcat:n3prffzhtjhmfcqndkaq23ogqq

A Large-Scale Network Data Analysis via Sparse and Low Rank Reconstruction

Liang Fu Lu, Zheng-Hai Huang, Mohammed A. Ambusaidi, Kui-Xiang Gou
2014 Discrete Dynamics in Nature and Society  
Moreover, the experiments have demonstrated the robustness of the algorithm as well even when the network traffic is polluted by the large volume anomalies and noise.  ...  In addition, we present the iterative scheme of the algorithm for network anomaly detection problem, which is termed as NAD-APG.  ...  Acknowledgments This work was partially supported by the National Natural Science Foundation of China (no. 11171252 and no. 61202379) and the Doctoral Fund of Ministry of Education of China (no. 20120032120041  ... 
doi:10.1155/2014/323764 fatcat:shs6wtxsmjhanbeupflkfdci2u

VCIP 2020 Index

2020 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)  
Spike Interval Analysis Zhao, Kaili Robust Visual Tracking Via An Imbalance-Elimination Mechanism Zhao, Min CNN-BASED ANOMALY DETECTION FOR FACE PRESENTATION ATTACK DETECTION WITH MULTI-CHANNEL IMAGES  ...  , Honggang Robust Visual Tracking Via An Imbalance- Elimination Mechanism Zhang, Huiqing No-Reference Objective Quality Assessment Method of Display Products Zhang, Jian Special Cane with Visual  ... 
doi:10.1109/vcip49819.2020.9301896 fatcat:bdh7cuvstzgrbaztnahjdp5s5y

Memorizing Normality to Detect Anomaly: Memory-augmented Deep Autoencoder for Unsupervised Anomaly Detection [article]

Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, Anton van den Hengel
2019 arXiv   pre-print
It has been observed that sometimes the autoencoder "generalizes" so well that it can also reconstruct anomalies well, leading to the miss detection of anomalies.  ...  Deep autoencoder has been extensively used for anomaly detection.  ...  However, unlike the sparse representation based anomaly detection methods [44, 26] , the proposed method obtains the desired sparse w via once efficient forward operation, instead of the iterative updating  ... 
arXiv:1904.02639v2 fatcat:bki7ibp3fnccljokd7erhkyk44

Hyperspectral Anomaly Detection based on Machine Learning: An Overview

Yichu Xu, Lefei Zhang, Bo Du, Liangpei Zhang
2022 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Hyperspectral anomaly detection (HAD) is an important hyperspectral image application.  ...  While most of the existed researches are related to statistic-based and distance-based techniques, by summarizing the background samples with certain models, and then, finding the very few outliers by  ...  Flowchart of sparse and collaborative representation-based HSI anomaly detection method. Fig. 4 . 4 Fig. 4. Flowchart of the reconstruction-based HSI anomaly detection method. Fig. 5 . 5 Fig. 5.  ... 
doi:10.1109/jstars.2022.3167830 fatcat:zdhdwbglrnbjfdf5w5trsopizi

Multi-scale sparse coding with anomaly detection and classification

Hojjat Akhondi-Asl, James D. B. Nelson
2016 2016 IEEE Statistical Signal Processing Workshop (SSP)  
We here place a recent joint anomaly detection and classification approach based on sparse error coding methodology into multi-scale wavelet basis framework.  ...  Anomaly detection in power networks provides a motivating application and tests on a real-world data set corroborates the efficacy of the proposed model.  ...  In [4] , Adler et al. applied the K-SVD method to learn a dictionary from ECG data and applied sparse coding with anomaly detection to detect irregular heartbeats.  ... 
doi:10.1109/ssp.2016.7551727 dblp:conf/ssp/AslN16 fatcat:lrprdsn6w5h5hgfwyycxkorezm

Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series [article]

Hong-Lan Botterman and Julien Roussel and Thomas Morzadec and Ali Jabbari and Nicolas Brunel
2022 arXiv   pre-print
We propose a robust principal component analysis (RPCA) framework to recover low-rank and sparse matrices from temporal observations.  ...  We develop an online version of the batch temporal algorithm in order to process larger datasets or streaming data.  ...  But at the same time, we want to have an exact detection of anomalies in the observed data betting also sparse.  ... 
arXiv:2208.01998v1 fatcat:piaxtf2wdfcdtert7yahixxn5a

Robust multivariate autoregression for anomaly detection in dynamic product ratings

Nikou Günnemann, Stephan Günnemann, Christos Faloutsos
2014 Proceedings of the 23rd international conference on World wide web - WWW '14  
This latent behavior is mixed with a sparse anomaly signal finally leading to the observed data.  ...  Given a time-series of rating distributions, in this work, we answer the following questions: (1) How to detect the base behavior of users regarding a product's evaluation over time?  ...  reflect the views of the National Science Foundation, DARPA, or other funding parties.  ... 
doi:10.1145/2566486.2568008 dblp:conf/www/GunnemannGF14 fatcat:4eebpvu5kndrzjo2g576c4spum

Adaptive Sparse Representations for Video Anomaly Detection

Xuan Mo, Vishal Monga, Raja Bala, Zhigang Fan
2014 IEEE transactions on circuits and systems for video technology (Print)  
Recently sparse reconstruction techniques have been used for image classification, and shown to provide excellent robustness to occlusion.  ...  This dissertation explores novel and adaptive sparse representations for addressing open challenges in video anomaly detection.  ...  Online Detection of Unusual Events in Videos via Dynamic Sparse Coding Zhao et al. [22] propose an online sparsity-based method for video anomaly detection.  ... 
doi:10.1109/tcsvt.2013.2280061 fatcat:njjuwnbe7fa7toikvl7lsdguhi

Hyperspectral Anomaly Detection via Dual Collaborative Representation

Guoyun Zhang, Nanying Li, Bing Tu, Zhuolang Liao, Yishu Peng
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
detection performance with respect to state-of-the-art anomaly detection methods in terms of detection accuracy.  ...  In this article, a dual collaborative representation (DCR)-based hyperspectral anomaly detection method is proposed to solve the above problem effectively, which consists of the following main steps.  ...  The false alarm rate of the proposed method can be effectively reduced while the robustness and detection performance of the method can be improved.  ... 
doi:10.1109/jstars.2020.3009324 fatcat:stvf7x2tw5bnvm532jwoey76wu

Hyperspectral Anomaly Detection via Background Estimation and Adaptive Weighted Sparse Representation

2018 Remote Sensing  
Some methods focus on mitigating the anomaly contamination in background estimation, such as the random selection-based anomaly detector (RSAD) [17], the robust nonlinear anomaly detection (RNAD) [18],  ...  In this paper, a novel hyperspectral anomaly detection method based on background estimation and adaptive weighted sparse representation has been proposed.  ...  Author Contributions: Lingxiao Zhu proposed the general idea of the method and performed the experiments. Gongjian Wen provided many constructive advices for the preparation.  ... 
doi:10.3390/rs10020272 fatcat:lu6eld6jnjgl7mzixqtpa5t5mq

Cross-domain traffic scene understanding by motion model transfer

Xun Xu, Shaogang Gong, Timothy Hospedales
2013 Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream - ARTEMIS '13  
This framework is capable of online "sparse-shot" anomaly detection and motion event classification in the unseen target domain, without the need for extensive data collection, labelling and offline model  ...  Extensive experiments show the effectiveness of the proposed framework for cross-domain motion event classification, anomaly detection and scene association.  ...  We call this cross-domain "sparse-shot" event classification and anomaly detection.  ... 
doi:10.1145/2510650.2510657 dblp:conf/mm/XuGH13 fatcat:jklhlcumevgcvgl6lo22sam5hm
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