Real-Time Detection and Spatial Segmentation of Difference Image Motion Changes

Kai Zhou,Enqing Chen,Rui Yuan,Zhendong Zhang,Yingping Huang

doi:10.1109/access.2020.3014654

Abstract

In this article, real-time detection of moving targets is performed, and an improved moving target detection algorithm combining background modeling and three-frame difference method is studied. An improved anisotropic differential filtering algorithm is proposed. The algorithm mainly compares the gradient difference between the target and the background in eight nearby directions. Choose the average of the three directions with the smallest spread function value to filter the space, which will effectively highlight the difference between the targets. so that the target signal is well preserved in the differential space domain; then based on obtaining the difference map. The average gray level of the target and the local signal-to-noise ratio of the spatial domain obtained by the energy enhancement algorithm combining the spatial and temporal motion characteristics have reached 278 and 14.84 dB, which further improves the target signal. Strict registration of heterogeneous spatial domains is completed by affine transformation and bilinear interpolation, and in many cases, the invariance and robustness of the method are verified. Focus on the proposed color space fusion method based on spatial frequency and fuzzy transformation. The superiority of the methods is evaluated and compared from both subjective and objective aspects. Finally, it lays a good foundation for moving target detection and better dealing with complex scenes. The improved inter-frame difference method combined with background subtraction validates and compares the method in different scenarios. It shows the advanced nature of the method in both qualitative and quantitative aspects. And effectively overcome the shortcomings of the particle filter tracking algorithm based on a single feature.

Highlights

The most important and intuitive way for humans to obtain information is through human vision
After continuous research by many scholars, we found that the detection accuracy and algorithm complexity of the moving target detection algorithm are improved
Moving object detection and tracking plays an important role in the field of computer vision

Summary

INTRODUCTION

The most important and intuitive way for humans to obtain information is through human vision. This article first studies several classic moving target detection algorithms analyzes their respective advantages and disadvantages through simulation experiments and theoretical basis, and selects the background detection method with high detection accuracy and good real-time performance as the main research algorithm in this article; several difficult problems encountered, such as dynamic background, ghosting problems, and intermittent target motion, etc., this article designs an adaptive model size background extractor (AMSBE) background difference method; to solve the actual scene, there are common shadow problems. AN IMPROVED DIFFERENTIAL SPACE DETECTION ALGORITHM The core idea of the inter-frame difference method is to use the difference in pixel values between adjacent frames to distinguish the background from the moving target. When the point value is greater than those compared pixels, the point is judged as a preliminary feature point and the step of screening

DESIGN OF REAL-TIME DETECTION OF MOVEMENT CHANGES

RESULTS

CONCLUSION