scholarly journals Spatio-Temporal Unity Networking for Video Anomaly Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 172425-172432 ◽  
Author(s):  
Yuanyuan Li ◽  
Yiheng Cai ◽  
Jiaqi Liu ◽  
Shinan Lang ◽  
Xinfeng Zhang
Keyword(s):  
Anomaly Detection ◽  
Temporal Unity ◽  
Spatio Temporal

Spatio-Temporal AutoEncoder for Video Anomaly Detection

2017 ◽  
Author(s):  
Yiru Zhao ◽  
Bing Deng ◽  
Chen Shen ◽  
Yao Liu ◽  
Hongtao Lu ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Spatio Temporal

scholarly journals A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data

2020 ◽  
Vol 10 (15) ◽  
pp. 5191
Author(s):  
Yıldız Karadayı ◽  
Mehmet N. Aydin ◽  
A. Selçuk Öğrenci
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Hurricane Katrina ◽  
Time Series Data ◽  
Hybrid Approach ◽  
Ground Truth ◽  
Outbreak Detection ◽  
Series Data ◽  
Detection Techniques ◽  
Spatio Temporal

Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most settings, spatial context is often expressed in terms of ZIP code or region coordinates such as latitude and longitude. However, traditional anomaly detection techniques cannot handle more than one contextual attribute in a unified way. In this paper, a new hybrid approach based on deep learning is proposed to solve the anomaly detection problem in multivariate spatio-temporal dataset. It works under the assumption that no prior knowledge about the dataset and anomalies are available. The architecture of the proposed hybrid framework is based on an autoencoder scheme, and it is more efficient in extracting features from the spatio-temporal multivariate datasets compared to the traditional spatio-temporal anomaly detection techniques. We conducted extensive experiments using buoy data of 2005 from National Data Buoy Center and Hurricane Katrina as ground truth. Experiments demonstrate that the proposed model achieves more than 10% improvement in accuracy over the methods used in the comparison where our model jointly processes the spatial and temporal dimensions of the contextual data to extract features for anomaly detection.

Anomaly Detection Based on Spatio-Temporal and Sparse Features of Network Traffic in VANETs

2019 ◽  
Author(s):  
Laisen Nie ◽  
Huizhi Wang ◽  
Shimin Gong ◽  
Zhaolong Ning ◽  
Mohammad S. Obaidat ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Network Traffic ◽  
Spatio Temporal ◽  
Sparse Features
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