Moving objects detection based on thresholding operations for video surveillance systems

2015 ◽  
Author(s):  
Omar ELHarrouss ◽  
Driss Moujahid ◽  
Soukaina Elidrissi Elkaitouni ◽  
Hamid Tairi
Keyword(s):  
Video Surveillance ◽  
Moving Objects ◽  
Surveillance Systems ◽  
Moving Objects Detection ◽  
Objects Detection

scholarly journals Fast Moving Objects Detection Using iLBP Background Model

2014 ◽  
Vol XL-3 ◽  
pp. 347-350 ◽  
Author(s):  
B. Vishnyakov ◽  
V. Gorbatsevich ◽  
S. Sidyakin ◽  
Y. Vizilter ◽  
I. Malin ◽  
...  
Keyword(s):  
Moving Objects ◽  
Computational Algorithm ◽  
Image Representation ◽  
Local Binary Patterns ◽  
Surveillance Systems ◽  
New Approach ◽  
Moving Objects Detection ◽  
Proposed Model ◽  
Objects Detection ◽  
Background Models

In this paper a new approach for moving objects detection in video surveillance systems is proposed. It is based on iLBP (intensity local binary patterns) descriptor that combines the classic LBP (local binary patterns) and the multiple regressive pseudospectra model. The iLBP descriptor itself is considered together with computational algorithm that is based on the sign image representation. We show that motion analysis methods based on iLBP allow uniformly detecting objects that move with different speed or even stop for a short while along with unattended objects. We also show that proposed model is comparable to the most popular modern background models, but is significantly faster.

scholarly journals DIFFUSION BACKGROUND MODEL FOR MOVING OBJECTS DETECTION

2015 ◽  
Vol XL-5/W6 ◽  
pp. 65-71 ◽  
Author(s):  
B. V. Vishnyakov ◽  
S. V. Sidyakin ◽  
Y. V. Vizilter
Keyword(s):  
Moving Objects ◽  
State Of The Art ◽  
Surveillance Systems ◽  
Analysis Method ◽  
Diffusion Maps ◽  
New Approach ◽  
Moving Objects Detection ◽  
Proposed Model ◽  
Objects Detection ◽  
Background Models

In this paper, we propose a new approach for moving objects detection in video surveillance systems. It is based on construction of the regression diffusion maps for the image sequence. This approach is completely different from the state of the art approaches. We show that the motion analysis method, based on diffusion maps, allows objects that move with different speed or even stop for a short while to be uniformly detected. We show that proposed model is comparable to the most popular modern background models. We also show several ways of speeding up diffusion maps algorithm itself.

Moving Objects Detection Algorithm Based on Three-Dimensional Gaussian Mixture Codebook Model Using XYZ Color Model

2013 ◽  
Vol 347-350 ◽  
pp. 3505-3509 ◽  
Author(s):  
Jin Huang ◽  
Wei Dong Jin ◽  
Na Qin
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Three Dimensional ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Color Model ◽  
Moving Objects Detection ◽  
Objects Detection ◽  
Model Algorithm

In order to reduce the difficulty of adjusting parameters for the codebook model and the computational complexity of probability distribution for the Gaussian mixture model in intelligent visual surveillance, a moving objects detection algorithm based on three-dimensional Gaussian mixture codebook model using XYZ color model is proposed. In this algorithm, a codebook model based on XYZ color model is built, and then the Gaussian model based on X, Y and Z components in codewords is established respectively. In this way, the characteristic of the three-dimensional Gaussian mixture model for the codebook model is obtained. The experimental results show that the proposed algorithm can attain higher real-time capability and its average frame rate is about 16.7 frames per second, while it is about 8.3 frames per second for the iGMM (improved Gaussian mixture model) algorithm, about 6.1 frames per second for the BM (Bayes model) algorithm, about 12.5 frames per second for the GCBM (Gaussian-based codebook model) algorithm, and about 8.5 frames per second for the CBM (codebook model) algorithm in the comparative experiments. Furthermore the proposed algorithm can obtain better detection quantity.

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