Results 21 to 30 of about 161,068 (368)
Video anomaly detection based on wake motion descriptors and perspective grids [PDF]
, 2014 This paper proposes a video anomaly detection method based on wake motion descriptors. The method analyses the motion characteristics of the video data, on a video volume- by-video volume basis, by computing the wake left behind by moving objects in the ...
Victor Sanchez +5 more
core +1 more source
, 2019 <p>Anomaly detection implemented in Keras</p ...
Chen, Xianshun, Chen, Xianshun (6828593)
core +1 more source
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
Anomaly detection with inexact labels [PDF]
Machine Learning, 2020 We propose a supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous. Although many anomaly detection methods have been proposed, they cannot handle inexact anomaly labels.
Tomoharu Iwata +3 more
openaire +2 more sources
Multivariate Time Series Anomaly Detection Algorithm in Missing Value Scenario [PDF]
Jisuanji kexueTime series anomaly detection is an important research field in industry.Current methods of time series anomaly detection focus on anomaly detection for complete time series data,without considering the time series anomaly detection task containing ...
ZENG Zihui, LI Chaoyang, LIAO Qing
doaj +1 more source
Real-time Anomaly Detection Framework via System Calls Based on Integrated Learning [PDF]
Jisuanji gongcheng, 2023 Anomaly detection based on system calls data cannot complete the synchronous perception task of intrusion behavior within the process lifecycle，and there is a problem of low real-time anomaly detection accuracy.
CHEN Zhonglei, YI Peng, CHEN Xiang, HU Tao
doaj +1 more source
Saliencycut: Augmenting Plausible Anomalies for Anomaly Detection
Pattern Recognition, 2023 Anomaly detection under open-set scenario is a challenging task that requires learning discriminative fine-grained features to detect anomalies that were even unseen during training. As a cheap yet effective approach, data augmentation has been widely used to create pseudo anomalies for better training of such models.
Jianan Ye +5 more
openaire +2 more sources
A dubiety-determining based model for database cumulated anomaly intrusion [PDF]
, 2007 The concept of Cumulated Anomaly (CA), which describes a new type of database anomalies, is addressed. A typical CA intrusion is that when a user who is authorized to modify data records under certain constraints deliberately hides his/her intentions ...
Yi, J +5 more
core +1 more source
KAN-based Unsupervised Multivariate Time Series Anomaly Detection Network [PDF]
Jisuanji kexueTime series data is widely present in fields such as finance,healthcare,industry,and transportation.Time Series Ano-maly Detection(TSAD) is crucial for ensuring system stability and safety.Most current time series anomaly detection methods are ...
WANG Cheng, JIN Cheng
doaj +1 more source