Real-time vision system oriented target compression sensing method

Abstract

The invention discloses a real-time vision system oriented target compression sensing method, which comprises the following steps: image reconstruction, mixed compression sensing, high-efficient Vibe target detection, updating and post-processing, wherein the step of the image reconstruction comprises the following specific steps: according to the size of a collected image, carrying out 4*4 partitioning on an image, and converting obtained image blocks into 16*1 vectors; the step of the mixed compression sensing comprises the following specific steps: constructing the image block corresponding to a mixed sampling matrix, and carrying out sampling compression; the step of the high-efficient Vibe target detection comprises the following specific steps: for each pixel in the image block, comparing a pixel value with a sample set to judge whether the pixel belongs to a background point or not; the step of updating comprises the following specific steps: according to the above detection result, determining a background block area and a target block area in the image block, and obtaining the parameter regulation information of the mixed sampling matrix of a next frame of image of the pixel according to a situation that the pixel belongs to the background block area or the target block area; and the step of the post-processing comprises the following specific steps: carrying out image optimization processing on each image block of the current frame to obtain a final target image of the current frame.

Description

technical field [0001] The invention relates to the technical field of target detection in visual images, in particular to a target compression sensing method for real-time visual systems. Background technique [0002] In the application field of real-time vision system related to computer vision, the detection of the target in the acquired visual image is the first step. The quality of the target detection algorithm affects further visual processing such as follow-up tracking and behavior recognition. Due to the complexity and changeability of the actual scene, the existing target detection algorithms are generally complex, with a large amount of calculation and high memory capacity requirements, and are not suitable for real-time vision systems with limited resources. [0003] Therefore, the target detection algorithm for the real-time vision system must first consider the problem of algorithm efficiency, and reduce the amount of calculation and storage capacity as much a...

AI Technical Summary

Problems solved by technology

Due to the complexity and changeability of the actual scene, the existing target detection algorithms are generally complex, with a large amount of calculation and high memory capacity requirements, and are not suitable for real-time vision systems with limited resources.

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