2,632 Hits in 4.8 sec

Quantifying the Tradeoff Between Cybersecurity and Location Privacy [article]

Dajiang Suo, M. Elena Renda, Jinhua Zhao
2021 arXiv   pre-print
While LBS providers could adopt privacy preservation mechanisms to obfuscate customer data, the accuracy of vehicle location data and trajectories is crucial for detecting anomalies, especially when machine  ...  When it comes to location-based services (LBS), user privacy protection can be in conflict with security of both users and trips.  ...  Bao, “Online anomalous trajectory IEEE Transactions on Cybernetics, 2020. detection with deep generative sequence modeling,” in 2020 IEEE 36th [22] Z.  ... 
arXiv:2105.01262v2 fatcat:yick5s644vfo5pq4rr7svrxcdi

Anomaly Detection in Video Sequences: A Benchmark and Computational Model [article]

Boyang Wan and Wenhui Jiang and Yuming Fang and Zhiyuan Luo and Guanqun Ding
2021 arXiv   pre-print
video sequences including normal and abnormal video clips with 14 anomaly categories including crash, fire, violence, etc. with large scene varieties, making it the largest anomaly analysis database to  ...  To tackle these problems, we contribute a new Large-scale Anomaly Detection (LAD) database as the benchmark for anomaly detection in video sequences, which is featured in two aspects. 1) It contains 2000  ...  optical flow image, while the second generator of GANs is fed into Early anomaly detection studies extract object trajectories to detect a real optical-flow image and generates a reconstructed appearance  ... 
arXiv:2106.08570v1 fatcat:oeju32uh65fpliiewsfmhupu44

Recent Advances in Anomaly Detection Methods Applied to Aviation

Luis Basora, Xavier Olive, Thomas Dubot
2019 Aerospace (Basel)  
Anomaly detection is an active area of research with numerous methods and applications.  ...  After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning  ...  model: [72] (2014) Online model: [73] (2016)  ... 
doi:10.3390/aerospace6110117 fatcat:kprkb643xrhcnmjy2c2lbzoa7m

Online Detection of Anomalous Sub-trajectories: A Sliding Window Approach Based on Conformal Anomaly Detection and Local Outlier Factor [chapter]

Rikard Laxhammar, Göran Falkman
2012 IFIP Advances in Information and Communication Technology  
Automated detection of anomalous trajectories is an important problem in the surveillance domain.  ...  It is an instance of the previously proposed Conformal anomaly detector and, hence, operates online with well-calibrated false alarm rate.  ...  The need for algorithms capable of efficient online anomaly detection in trajectories, i.e. when trajectories are analysed in sequence and model parameters updated incrementally, was highlighted by Piciarelli  ... 
doi:10.1007/978-3-642-33412-2_20 fatcat:xzmutbfhvza5nbt2prqwn5nipu

Continual Learning for Anomaly Detection in Surveillance Videos [article]

Keval Doshi, Yasin Yilmaz
2020 arXiv   pre-print
Our proposed algorithm leverages the feature extraction power of neural network-based models for transfer learning, and the continual learning capability of statistical detection methods.  ...  Motivated by these research gaps, we propose an online anomaly detection method for surveillance videos using transfer learning and continual learning, which in turn significantly reduces the training  ...  The selection of features significantly impacts the identifiability of types of anomalous events in video sequences.  ... 
arXiv:2004.07941v1 fatcat:nga25izgjja2nkxv7wor55xg6i

A Survey of Single-Scene Video Anomaly Detection [article]

Bharathkumar Ramachandra, Michael J. Jones, Ranga Raju Vatsavai
2020 arXiv   pre-print
This survey article summarizes research trends on the topic of anomaly detection in video feeds of a single scene.  ...  They also cluster potentially anomalous trajectories to detect anomalous regions and filter out small false positive detections.  ...  They then use Deep GMMs [95] to model a generative process of normal patterns, maximizing a lowerbound on log-likelihood.  ... 
arXiv:2004.05993v2 fatcat:b3jjyp5jfvd2llfsg5ek235s4y

Novel Anomalous Event Detection based on Human-object Interactions

Rensso Mora Colque, Carlos Caetano, Victor C. de Melo, Guillermo Camara Chavez, William Robson Schwartz
2018 Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications  
Such patterns are used afterwards to detect anomalous events in a different scene.  ...  Our paradigm shift anomalous event detection approach exploits human-object interactions to learn normal behavior patterns from a specific context.  ...  ACKNOWLEDGMENTS The authors would like to thank the Brazilian National Research Council -CNPq (Grant #311053/2016-5), Novel Anomalous Event Detection based on Human-object Interactions 299 the Minas Gerais  ... 
doi:10.5220/0006615202930300 dblp:conf/visapp/ColqueCMCS18 fatcat:g4727jvfoballmrqivkms3o6ka

Sequential Adversarial Anomaly Detection for One-Class Event Data [article]

Shixiang Zhu, Henry Shaowu Yuchi, Minghe Zhang, Yao Xie
2021 arXiv   pre-print
The proposed method can generally apply to detecting anomalous sequences.  ...  The generator captures the dependence in sequential events using the marked point process model.  ...  We aim to develop a detector that can detect the anomalous sequence in an online fashion and detect the anomaly as soon as possible. Denote such a detector as with parameter θ.  ... 
arXiv:1910.09161v4 fatcat:bqxyk2olvvhghhy7aia2tbuvqq

Unsupervised Online Anomaly Detection On Irregularly Sampled Or Missing Valued Time-Series Data Using LSTM Networks [article]

Oguzhan Karaahmetoglu
2020 arXiv   pre-print
Furthermore, we provide a training algorithm for the online setup, where we optimize our model parameters with individual sequences as the new data arrives.  ...  We study anomaly detection and introduce an algorithm that processes variable length, irregularly sampled sequences or sequences with missing values.  ...  CONCLUDING REMARKS In this paper, we introduce an unsupervised and online anomaly detection algorithm, which can detect anomalous sequences that are irregularly sampled or have missing values.  ... 
arXiv:2005.12005v1 fatcat:dd3es7w5l5d6tlug7pebpkwjlu

Deep Video Anomaly Detection: Opportunities and Challenges [article]

Jing Ren, Feng Xia, Yemeng Liu, Ivan Lee
2021 arXiv   pre-print
Recently, many studies on extending deep learning models for solving anomaly detection problems have emerged, resulting in beneficial advances in deep video anomaly detection techniques.  ...  Specifically, we summarise the opportunities and challenges of deep learning models on video anomaly detection tasks, respectively.  ...  ACKNOWLEDGMENT The authors would like to thank Teng Guo, Shuo Yu, and Ke Sun for their help with the first draft of this paper.  ... 
arXiv:2110.05086v1 fatcat:5tpj4bqdd5csbp6efvvcqvufeq

Plug-and-Play Anomaly Detection with Expectation Maximization Filtering [article]

Muhammad Umar Karim Khan, Mishal Fatima, Chong-Min Kyung
2020 arXiv   pre-print
We believe our work is the first step towards using deep learning methods with autonomous plug-and-play smart cameras for crowd anomaly detection.  ...  We propose a Core Anomaly-Detection (CAD) neural network which learns the motion behavior of objects in the scene with an unsupervised method.  ...  To our knowledge, this is the first work that proposes an online crowd anomaly detection system with deep learning.  ... 
arXiv:2006.08933v1 fatcat:jgrdevhvffebzgi7brasnbtsma

Research on Real-Time Anomaly Detection of Fishing Vessels in a Marine Edge Computing Environment

Jie Huang, Fengwei Zhu, Zejun Huang, Jian Wan, Yongjian Ren
2021 Mobile Information Systems  
The model runs in the edge layer, making full use of the information of moving edge nodes and nearby nodes, and combines a historical trajectory extraction detection model with an online anomaly detection  ...  Online anomaly detection algorithms detect anomalous behavior in specific scenarios based on the spatiotemporal neighborhood similarity and reduce the impact of anomaly evolution.  ...  Besides, many abnormal behaviors are unknown and change with time, so studies about online anomalous trajectory detection have been proposed [19, 20] .  ... 
doi:10.1155/2021/5598988 doaj:a9a32aeb22d6497cb775a8837f84f863 fatcat:i4da2ijugvbaveyy5ewo4om5du

Out-of-Distribution Dynamics Detection: RL-Relevant Benchmarks and Results [article]

Mohamad H Danesh, Alan Fern
2021 arXiv   pre-print
This problem is particularly important in the context of deep RL, where learned controllers often overfit to the training environment.  ...  Our first contribution is to design a set of OODD benchmarks derived from common RL environments with varying types and intensities of OODD.  ...  Each anomalous trajectory is generated by randomly selecting the appropriate size subset of features to modify, which provides diversity across the anomalous trajectories.  ... 
arXiv:2107.04982v1 fatcat:ony2bjgmkrakveuocbrbflb5qu

Intelligent video surveillance: a review through deep learning techniques for crowd analysis

G. Sreenu, M. A. Saleem Durai
2019 Journal of Big Data  
Majority of the papers reviewed in this survey are based on deep learning technique. Various deep learning methods are compared in terms of their algorithms and models.  ...  The paper includes a deep rooted survey which starts from object recognition, action recognition, crowd analysis and finally violence detection in a crowd environment.  ...  Online real time crowd behavior detection in video sequences [115] suggests FSCB, behavior detection through feature tracking and image segmentation.  ... 
doi:10.1186/s40537-019-0212-5 fatcat:mh7d5d5c5zeczf5sdmgwz3claq

Editorial for application-driven knowledge acquisition

Xue Li, Sen Wang, Bohan Li
2020 World wide web (Bussum)  
In this background, most researches are driven by specific applications and further integrated deep learning models with big data.  ...  Following an open call for papers, the articles in this special issue focus on various deep learning models including recurrent neural network (RNN), long and short time memory network (LSTM), bidirectional  ...  Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.  ... 
doi:10.1007/s11280-020-00827-6 fatcat:gdt63za6mrewriu7frir2nhkby
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