Results 41 to 50 of about 4,737,988 (265)
Pose‐driven human activity anomaly detection in a CCTV‐like environment
IET Image Processing, 2023 Human activity anomaly detection plays a crucial role in the next generation of surveillance and assisted living systems. Most anomaly detection algorithms are generative models and learn features from raw images.
Yuxing Yang +2 more
doaj +1 more source
A steam turbine anomaly detection method based on O-DAE and SVDD
Zhejiang dianli, 2023 Anomaly detection in unlabeled and highly imbalanced monitoring data is one of the most urgent to be solved and challenging industry problems. The use of autoencoders for anomaly detection is becoming more and more popular due to the powerful high ...
XU Weimin +5 more
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Diversity-Measurable Anomaly Detection [PDF]
Computer Vision and Pattern Recognition, 2023 Reconstruction-based anomaly detection models achieve their purpose by suppressing the generalization ability for anomaly. However, diverse normal patterns are consequently not well reconstructed as well. Although some efforts have been made to alleviate
Wenrui Liu +4 more
semanticscholar +1 more source
Spoofing Attack Detection by Anomaly Detection [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 Spoofing attacks on biometric systems can seriously compromise their practical utility. In this paper we focus on face spoofing detection. The majority of papers on spoofing attack detection formulate the problem as a two or multiclass learning task, attempting to separate normal accesses from samples of different types of spoofing attacks.
Fatemifar, Soroush +3 more
openaire +4 more sources
Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System
Sensors, 2021 The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals.
Hoon Ko, Kwangcheol Rim, Isabel Praça
doaj +1 more source
Detecting Relative Anomaly [PDF]
, 2017 System states that are anomalous from the perspective of a domain expert occur frequently in some anomaly detection problems. The performance of commonly used unsupervised anomaly detection methods may suffer in that setting, because they use frequency as a proxy for anomaly.
Yixin Shi +2 more
openaire +2 more sources
MIST: Multiple Instance Self-Training Framework for Video Anomaly Detection [PDF]
Computer Vision and Pattern Recognition, 2021 Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations. Most existing works are limited in insufficient video representations. In this work, we develop a multiple instance
Jianfeng Feng +2 more
semanticscholar +1 more source
Anomaly detection and community detection in networks
Journal of Big Data, 2022 AbstractAnomaly detection is a relevant problem in the area of data analysis. In networked systems, where individual entities interact in pairs, anomalies are observed when pattern of interactions deviates from patterns considered regular. Properly defining what regular patterns entail relies on developing expressive models for describing the observed ...
Safdari, Hadiseh, De Bacco, Caterina
openaire +4 more sources
E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System
IEEE Access, 2021 Industrial Control Systems (ICS) are evolving into smart environments with increased interconnectivity by being connected to the Internet. These changes increase the likelihood of security vulnerabilities and accidents. As the risk of cyberattacks on ICS
Chanwoong Hwang, Taejin Lee
doaj +1 more source
, 2021 Anomaly detection (AD) is considered one of the important research areas that have a diverse range of application domains. Some of the anomaly detection techniques presented in the literature were specifically implemented for certain domains, whereas others were more generic.
Abu Musa, Tahani Hussein +1 more
openaire +3 more sources