Particle filter target tracking improvement method based on vision attention mechanism

Abstract

A particle filter target tracking improvement method based on a vision attention mechanism includes steps: 1, initializing input; and 2, modeling a feature and filtering particles based on a visual saliency mechanism, selecting an optimum feature according to saliency order of features, modeling the feature and filtering mismatching particles to obtain an optimum particle set finally. The features comprise a color feature, a texture feature and a movement feature. A target position is calculated according to the optimum particle set, tracking results are output, and simultaneously the saliency order of the features is updated according to the optimum feature. The particle filter target tracking improvement method is a multi-feature target tracking method for simulating a human vision mechanism, combines the color, texture and movement features, and can guarantee real-time performance, accuracy and robustness.

Description

technical field [0001] The invention mainly relates to the field of computer vision methods, in particular to an improved particle filter target tracking method based on a visual attention mechanism. Background technique [0002] Object tracking is an important topic in computer vision research, it is the basis of object behavior understanding, and it is an important part of image system's continuous and accurate work. Visual tracking usually refers to the precise positioning of a region of interest or a target object in a continuously changing video sequence. In the computer vision recognition and processing system, target tracking is in the processing link between image motion detection and target behavior understanding, and is an important part of continuous and accurate work of the image system. , intelligent vehicles, virtual reality human-computer interaction and other applications have very important value and significance. How to effectively improve the accuracy of...

AI Technical Summary

Problems solved by technology

However, the method of using a single feature cannot be continuously and robustly qualified for the target tracking problem in a complex and changeable environment. The existing multi-feature fusion algorithm is usually at the cost of increasing the computational complexity. When too many features are incorporated, the real-time sex is not guaranteed

The target tracking algorithm based on visual attention model and particle filter proposes to use the salient features of the image to improve the particle filter tracking algorithm. The calculation of image salient features is still complicated; particle filter based on dynamic salient features and target tracking based on visual saliency The algorithm mainly focuses on how to use particle filter to track the target with salient features, but it is not suitable for tracking a specific target; the use of visually salient particle filter is the latest method to combine salient features with particle filter. method for target tracking, but it only uses a single color and brightness as the feature vector

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