Particle filter target tracking improvement method based on vision attention mechanism

A visual attention mechanism, particle filtering technology, applied in the field of computer vision, can solve the problems of single color and brightness, complex calculation, inapplicable tracking problems, etc., to achieve strong real-time, high accuracy and robustness, and ensure real-time Effect

Inactive Publication Date: 2012-06-27
HUNAN UNIV
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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 algorit

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  • Particle filter target tracking improvement method based on vision attention mechanism
  • Particle filter target tracking improvement method based on vision attention mechanism
  • Particle filter target tracking improvement method based on vision attention mechanism

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Embodiment Construction

[0034] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0035] Such as figure 1 Shown, the present invention is based on the improved particle filter target tracking method of visual attention mechanism, and its steps are:

[0036] 1. Initialize input;

[0037] First, manually select the target to be tracked (shown in step ①);

[0038] Then initialize the image particles in the selected area (step ②);

[0039] If the image acquisition is successful (step ③), that is, the image information of the specified target is successfully obtained, then the initialization particles are preprocessed, otherwise, it ends (step ⑩). Preprocessing includes filtering image noise (step ④), which is used to provide more accurate input for the next step.

[0040] Update particle distribution (step ⑤) to adapt to scene changes;

[0041] 2. Feature modeling and particle filtering based on visual saliency me...

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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...

Claims

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Application Information

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IPC IPC(8): G06T7/20G06T7/00
Inventor 肖德贵秦云川田峥杨翔陈琳熊鹏文龙蔡幼奇
Owner HUNAN UNIV
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