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

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.

Abstract licence: CC BY-NC-ND

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