Target tracking method based on frequency domain saliency

Abstract

The invention relates to a target tracking method based on frequency domain saliency, which comprises the steps of S1-S4, establishing direction feature maps, color feature maps, gray feature maps and motion feature maps; S5-S6, establishing static and moving polynomials and performing Fourier transform to the static and moving polynomials; S7, performing Gaussian low-pass filtration and inverse Fourier transform to magnitude spectra to obtain static saliency maps and moving saliency maps; S8, multiplying the moving saliency maps by the static saliency maps with the corresponding scales to obtain saliency multi-scale detection result saliency map; S9, calculating the one-dimensional entropy function of the histogram of the saliency map and extracting a time domain saliency map corresponding to a minimum information entropy as an optimal saliency map at the moment t; S10, using products of average weight of t-1 and t-2 frame saliency maps and the optimal saliency map at the moment t as visual saliency maps; S11, calculating difference of central positions of the visual saliency maps of adjacent frames, judging whether the tracking is failed or not and recording a failure saliency map; and S12, comparing the visual saliency map of the current frame with the failure saliency map and judging whether a target returns back to a visual field or not.

Description

technical field [0001] The invention relates to a method of using Gabor wavelet and Fourier transform method to detect salient targets and to track salient targets by using visual information, without using any prior knowledge of target images and without target templates. It is a technology for precise tracking of salient targets by scientific models, which is used in computer vision and target detection and tracking. Background technique [0002] The visual attention model is a model that uses a computer to simulate the human visual attention system. It extracts the attention-grabbing focus that the human eye can observe in an image. Compared with the computer, it is the salience of the image. area. Visual psychology studies have shown that when analyzing complex input scenes, the human visual system adopts a serial computing strategy, that is, using the selective attention mechanism to select a specific area of ​​the scene according to the local characteristics of the im...

AI Technical Summary

Problems solved by technology

However, there have always been problems in the application of saliency models in tracking. Since the prior knowledge of the image is not used in the process of saliency detection, how to distinguish objects from backgrounds in tracking, and distinguish tracking objects from non-tracking objects has become a problem in the application of saliency in tracking. biggest problem

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