Deep Learning-Driven Gaussian Modeling and Improved Motion Detection Algorithm of the Three-Frame Difference Method

Author:

Zheng Dingchao1,Zhang Yangzhi1,Xiao Zhijian1ORCID

Affiliation:

1. Digital Engineering Academy, Zhejiang Dongfang Polytechnic, Wenzhou 325200, China

Abstract

To enhance the effect of motion detection, a Gaussian modeling algorithm is proposed to fix holes and breaks caused by the conventional frame difference method. The proposed algorithm uses an improved three-frame difference method. A three-frame image sequence with one frame interval is selected for pairwise difference calculation. The logical “OR” operation is used to achieve fast motion detection and to reduce voids and fractures. The Gaussian algorithm establishes an adaptive learning model to make the size and contour of the motion detection more accurate. The motion extracted by the improved three-frame difference method and Gaussian model is logically summed to obtain the final motion foreground picture. Moreover, a moving target detection method, based on the U-Net deep learning network, is proposed to reduce the dependency of deep learning on the number of training datasets. It helps the algorithm to train models on small datasets. Next, it calculates the ratio of the number of positive and negative samples in the dataset and uses the reciprocal of the ratio as the sample weight to deal with the imbalance of positive and negative samples. Finally, a threshold is set to predict the results for obtaining the moving object detection accuracy. Experimental results show that the algorithm can suppress the generation and rupture of holes and reduce the noise. Also, it can quickly and accurately detect movement to meet the design requirements.

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Computer Science Applications

Reference22 articles.

1. Motion detection by four-frame difference and improved Gaussian mixture model;L. Xiao;Science Technology and Engineering,2020

2. Motion tracking algorithm based on optical flow feature points in power monitoring scenes;Y. Jin;Electric Power Science and Engineering,2020

3. Overview of pixel-based background subtraction technology[J];S. Sui;Science and Technology Innovation,2019

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