Document Type : Research article


1 Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran

2 School of Automotive Engineering, Iran University of Science and Technology, Tehran, Iran

3 Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran

4 Department of Computer Science and Electronic Engineering, University of Essex, Essex, UK


This paper is an extension of our previous research on presenting a novel Gaussian Mixture-based (MOG2) Video Coding for CCTVs. The aim of this paper is to optimize the MOG2 algorithm used for foreground-background separation in video streaming. In fact, our previous study showed that traditional video encoding with the help of MOG2 has a negative effect on visual quality. Therefore, this study is our main motivation for improving visual quality by combining the previously proposed algorithm and color optimization method to achieve better visual quality. In this regard, we introduce Artificial Intelligence (AI) video encoding using Color Clustering (CC), which is used before the MOG2 process to optimize color and make a less noisy mask. The results of our experiments show that with this method the visual quality is significantly increased, while the latency remains almost the same. Consequently, instead of using morphological transformation which has been used in our past study, CC achieves better results such that PSNR and SSIM values have been shown to rise by approximately 1dB and 1 unit respectively.


  • Introducing new caching technology by separation of foreground and background video, based on moving and fixed objects
  • Detection of moving objects using Gaussian Mixture-based (MOG2) analysis by creating a mask and multiplying the masks into the whole part of the video by arithmetic operations to separate foreground from the background video
  • Optimization of MOG2 process by using color segmentation analysis namely Color Clustering (CC)
  • Reduction of almost 1/2 in I-frame transmission by using morphological transformation and MOG2 transmission (MT+MOG2) and 1/3 by using CC+MOG2 method
  • Improving the visual quality of transmission by CC+MOG2 transmission method by investigation of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) methods


Main Subjects

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