scholarly journals End-to-End Background Subtraction via a Multi-Scale Spatio-Temporal Model

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 97949-97958 ◽  
Author(s):  
Yizhong Yang ◽  
Tao Zhang ◽  
Jinzhao Hu ◽  
Dong Xu ◽  
Guangjun Xie
Keyword(s):  
Background Subtraction ◽  
Multi Scale ◽  
Temporal Model ◽  
End To End ◽  
Spatio Temporal

scholarly journals End-to-End Deep One-Class Learning for Anomaly Detection in UAV Video Stream

2021 ◽  
Vol 7 (5) ◽  
pp. 90
Author(s):  
Slim Hamdi ◽  
Samir Bouindour ◽  
Hichem Snoussi ◽  
Tian Wang ◽  
Mohamed Abid
Keyword(s):  
Anomaly Detection ◽  
Optical Flow ◽  
Video Processing ◽  
Background Subtraction ◽  
Video Stream ◽  
Learning Method ◽  
End To End ◽  
Spatio Temporal ◽  
Uav Images

In recent years, the use of drones for surveillance tasks has been on the rise worldwide. However, in the context of anomaly detection, only normal events are available for the learning process. Therefore, the implementation of a generative learning method in an unsupervised mode to solve this problem becomes fundamental. In this context, we propose a new end-to-end architecture capable of generating optical flow images from original UAV images and extracting compact spatio-temporal characteristics for anomaly detection purposes. It is designed with a custom loss function as a sum of three terms, the reconstruction loss (Rl), the generation loss (Gl) and the compactness loss (Cl) to ensure an efficient classification of the “deep-one” class. In addition, we propose to minimize the effect of UAV motion in video processing by applying background subtraction on optical flow images. We tested our method on very complex datasets called the mini-drone video dataset, and obtained results surpassing existing techniques’ performances with an AUC of 85.3.

The continuous spatio-temporal model (CSTM) as an exhaustive framework for multi-scale spatio-temporal analysis

2014 ◽  
Vol 28 (5) ◽  
pp. 1047-1060 ◽  
Author(s):  
N. Van de Weghe ◽  
B. de Roo ◽  
Y. Qiang ◽  
M. Versichele ◽  
T. Neutens ◽  
...  
Keyword(s):  
Temporal Analysis ◽  
Multi Scale ◽  
Temporal Model ◽  
Spatio Temporal

scholarly journals A comparison of multiple neighborhood matrix specifications for spatio-temporal model fitting: a case study on COVID-19 data

2021 ◽  
Author(s):  
Álvaro Briz-Redón ◽  
Adina Iftimi ◽  
Juan Francisco Correcher ◽  
Jose De Andrés ◽  
Manuel Lozano ◽  
...  
Keyword(s):  
Model Fitting ◽  
Temporal Model ◽  
Spatio Temporal

scholarly journals A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2333
Author(s):  
Lilu Zhu ◽  
Xiaolu Su ◽  
Yanfeng Hu ◽  
Xianqing Tai ◽  
Kun Fu
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Temporal Correlation ◽  
Local Association ◽  
Sensing Data ◽  
Multi Scale ◽  
Correlation Networks ◽  
Efficient Integration ◽  
Spatio Temporal ◽  
Query Algorithm

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.

scholarly journals Frequency-domain loss function for deep exposure correction of dark images

2021 ◽  
Author(s):  
Ojasvi Yadav ◽  
Koustav Ghosal ◽  
Sebastian Lutz ◽  
Aljosa Smolic
Keyword(s):  
Loss Function ◽  
Correct Choice ◽  
Frequency Space ◽  
Multi Scale ◽  
Quantitative Metrics ◽  
Image Translation ◽  
Domain Loss ◽  
In The Wild ◽  
End To End ◽  
Classical Image

AbstractWe address the problem of exposure correction of dark, blurry and noisy images captured in low-light conditions in the wild. Classical image-denoising filters work well in the frequency space but are constrained by several factors such as the correct choice of thresholds and frequency estimates. On the other hand, traditional deep networks are trained end to end in the RGB space by formulating this task as an image translation problem. However, that is done without any explicit constraints on the inherent noise of the dark images and thus produces noisy and blurry outputs. To this end, we propose a DCT/FFT-based multi-scale loss function, which when combined with traditional losses, trains a network to translate the important features for visually pleasing output. Our loss function is end to end differentiable, scale-agnostic and generic; i.e., it can be applied to both RAW and JPEG images in most existing frameworks without additional overhead. Using this loss function, we report significant improvements over the state of the art using quantitative metrics and subjective tests.

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