Particle Filter Tracking Method with Adaptive Adjustment of Tracking Window Size

Abstract

The invention discloses a particle filter tracking method for adaptively adjusting the tracking window size, which mainly solves the problem of real-time adaptive adjustment of the particle filter tracking window. The window adjustment method is: under the particle filter tracking framework, first establish the state transition equation of the moving target, initialize the target state; then use the state transition equation to predict the particle set at the next moment, calculate the particle likelihood function value and update it according to its value The weight of the particle, calculate the average distance d from the particle with a large weight to the center of the particle set; finally let the size of the window change linearly with the value of d, so as to realize the adaptive adjustment of the tracking window. This method realizes the adjustment of the particle filter tracking window with a small amount of calculation. Compared with similar methods, it has the advantages of simple calculation and fast tracking speed.

Description

technical field [0001] The invention relates to the technical field of video target tracking, in particular to a particle filter tracking method with adaptive adjustment of tracking window size. Background technique [0002] Target tracking is an important branch of machine vision research and the basis of advanced machine vision research. It has a wide range of applications, such as military guidance, road traffic monitoring, industrial automation production monitoring, passenger flow statistics, etc. Among the many tracking algorithms proposed by people, particle filter-based and Mean-Shift algorithms are research hotspots. [0003] In the particle filter target tracking algorithm, the target tracking window is determined by the initial size of the tracking target, and the size of the tracking window remains constant throughout the tracking process. However, when the size of the moving target becomes smaller and smaller, if the tracking window is fixed, some background no...

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Problems solved by technology

[0010] The Chinese invention patent with the application publication number CN102184554A discloses a method for adjusting the particle filter tracking window. This method uses the Otsu threshold method to segment the target area, and calculates the segmentation marker map to obtain the target scale observation value, and then uses the Kalman The filtering method adjusts the tracking window adaptively. The problem is that the size of the segmented target area is greatly affected by the noise, so the error of using it as the observed value is relatively large, and because the Kalman filtering method is used to adjust the window, the increase of Calculations;

[0011] The Chinese invention patent with application publication number CN102005055A discloses a method for adjusting the particle filter tracking window. The method calculates the covariance matrix of the particles based on the EM algorithm, and then calculates the global kernel window width of the particle set according to the covariance matrix. The calculation of the particle filter algorithm increases the amount of calculation, which affects the tracking speed of the algorithm.

[0012] The existing tracking window adjustment method requires more complex calculations, thus affecting the tracking speed

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