Particle filter target tracking method based on multi-feature fusion

Abstract

The invention discloses a particle filter target tracking method based on multi-feature fusion. The method comprises the following steps: acquiring a video image and performing filtering processing; calibrating a tracking target in the initial frame by using a rectangular frame, and calculating an edge histogram, a texture histogram and a depth histogram of the target template; updating a particlestate by adopting a second-order autoregression model, and obtaining a feature histogram of each particle; calculating the similarity of the two templates, obtaining a single feature discrimination degree according to the position mean value, the standard deviation and the overall position mean value of the particles under the single feature, and adaptively adjusting the fusion weight; determining the particle weight at the current moment in combination with the observation model of multi-feature fusion and the particle weight at the previous moment; sorting the particle weights, counting thenumber of particles with small weights, comparing the number of particles with small weights with a threshold value, correcting the size of a window, and determining the state of a tracking target. According to the method, the edge, texture and depth characteristics are combined, so that the target can be tracked more accurately and continuously.

Description

technical field [0001] The invention belongs to the technical field of moving target tracking, in particular to a particle filter target tracking method based on multi-feature fusion. Background technique [0002] Moving object tracking technology is an important research content in the field of computer vision, which involves many disciplines, such as image processing, pattern recognition, artificial intelligence, artificial neural network, etc. Target tracking technology has high practicability and a wide range of applications. It has greatly improved the level of automatic control in the fields of artificial intelligence, unmanned driving, and medical treatment, and is playing an increasingly important role in military and civilian fields. At present, the development trend of target tracking technology in the field of computer vision is mainly manifested in the fusion of scene information and target state, the fusion of multi-dimensional and multi-level information, the f...

AI Technical Summary

Problems solved by technology

In recent years, many scholars have proposed many new target feature selection methods and feature fusion rules in response to the application requirements of particle filter target tracking in different scenarios: Considering the differences in the description of different target features, a method for particle filter target tracking in the framework of particle filter target tracking is proposed. The color and edge features are fused to obtain a better tracking effect, but because the color and edge features are not strong in anti-occlusion ability, when the target has a large degree of occlusion, it is easy to lose the tracking target; or use sparse structure to express Texture features are embedded in the particle filter target tracking framework to achieve target tracking tasks. This algorithm can effectively avoid the defect of poor tracking robustness caused by illumination changes, but requires that the algorithm initializes the number of particles must be sufficient, and the number of too many particles is easy to reduce Algorithm Timeliness

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