A Moving Target Tracking Method in Complex Background

Abstract

The invention discloses a moving target tracking method applied to a complex background. According to moving target tracking methods such as a traditional Meanshift moving target tracking method, the tracking of a moving target is realized through tracking the color constraints of the target, and as a result, the problem of low tracking precision is brought about, in order to solve the problem, the invention provides the moving target tracking method applied to the complex background. The method includes the following steps that: feature point information in a Meanshift search window area is obtained based on an optical flow method; background feature point information is removed according to a velocity estimation value in a Kalman filter, and spatial constraint conditions only considering the feature points of a target to be tracked is constructed; and the obtained spatial constraints are combined with color constraints in the traditional Meanshift method, so that an improved backprojection histogram can be obtained, and the improved backprojection histogram, adopted as the probability density map of the Meanshift, is iterated, and therefore, the process of moving target tracking in the complex background can be completed.

Description

technical field [0001] The invention relates to the field of moving target monitoring, in particular to a moving target tracking method under a complex background. Background technique [0002] At present, typical moving target tracking and positioning methods include optical flow method, frame difference method, particle filter method, Kalman filter and Meanshift method, etc. Among them, the optical flow method and the frame difference method track based on the motion information of the object. They can track all foreground targets at the same time, but they cannot achieve target selective tracking (only all foreground objects can be tracked but not specific moving targets). Moreover, both methods are extremely susceptible to interference from background changes; the particle filter method requires a sufficient number of particles to ensure the robustness of the method, which greatly increases the amount of calculation in the tracking process; the Kalman filter method needs...

AI Technical Summary

Problems solved by technology

[0003] However, the traditional Meanshift method realizes the tracking of the moving target through the single constraint condition of the hue feature of the moving target model. When it is applied to the tracking process of the moving target in the complex background, especially when the hue information When they are similar or the same, the tracking target is usually lost because the background area and the tracking target area have too much similar hue information

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