Study to Find Optimal Solution for Multi-objects Detection by Background Image Subtraction with CNN in Real-Time Surveillance System

2021 ◽  
pp. 221-229
Author(s):  
Ravindra Sangle ◽  
Ashok Kumar Jetawat
2013 ◽  
Vol 694-697 ◽  
pp. 1974-1977
Author(s):  
Ze Xian Ke ◽  
Han Hong Jiang ◽  
Chao Liang Zhang

The paper proposes a new method for moving objects detection based on fusion of three frames differencing and Gaussian Mixture Model (GMM). In the method, two images are obtained by three frames differencing, then the adaptive background are modeled and updated by GMM for each pixel in the two differencing images. Next, two differencing images are done logic "and" operation to get the shape of the moving object. Finally adopt the mathematical morphology operation to eliminate noise and the small areas of non-objects motion parts. The simulation results show that the proposed method can detect the objects effectively and real-time. So it can be applied in visual surveillance system effectively.


2013 ◽  
Vol 694-697 ◽  
pp. 1937-1944
Author(s):  
Dan Yan ◽  
Qiang Yu ◽  
Ming Hui Wang

In surveillance system,it was challenging to improve real-time in the presence of dynamic background motions.We presented a real-time algorithm for foreground-background segmentation based on codebook model.Pixels were converted from RGB space to YCrCb space,background model used layered model.Firstly we established a basic codebook background model and then got rough background pixels by twice frame difference,and then only trained rough background pixels which have removed foreground pixels.Secondly the foreground was segmented from the background and we updated the background in real-time. The experimental results show that this method can save time of establishment of codebook background model and has small calculation and high accuracy in scenes such as illumination changes,swaying trees and stopped objects should be considered part of the background objects.


2006 ◽  
Vol 64 ◽  
pp. S88-S89
Author(s):  
J.A. Alava ◽  
C. Ezpeleta ◽  
I. Atutxa ◽  
C. Busto ◽  
E. Gómez ◽  
...  

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
François Stüder ◽  
Jean-Louis Petit ◽  
Stefan Engelen ◽  
Marco Antonio Mendoza-Parra

AbstractSince December 2019, a novel coronavirus responsible for a severe acute respiratory syndrome (SARS-CoV-2) is accountable for a major pandemic situation. The emergence of the B.1.1.7 strain, as a highly transmissible variant has accelerated the world-wide interest in tracking SARS-CoV-2 variants’ occurrence. Similarly, other extremely infectious variants, were described and further others are expected to be discovered due to the long period of time on which the pandemic situation is lasting. All described SARS-CoV-2 variants present several mutations within the gene encoding the Spike protein, involved in host receptor recognition and entry into the cell. Hence, instead of sequencing the whole viral genome for variants’ tracking, herein we propose to focus on the SPIKE region to increase the number of candidate samples to screen at once; an essential aspect to accelerate diagnostics, but also variants’ emergence/progression surveillance. This proof of concept study accomplishes both at once, population-scale diagnostics and variants' tracking. This strategy relies on (1) the use of the portable MinION DNA sequencer; (2) a DNA barcoding and a SPIKE gene-centered variant’s tracking, increasing the number of candidates per assay; and (3) a real-time diagnostics and variant’s tracking monitoring thanks to our software RETIVAD. This strategy represents an optimal solution for addressing the current needs on SARS-CoV-2 progression surveillance, notably due to its affordable implementation, allowing its implantation even in remote places over the world.


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