Gray target tracking method based on self-adaptive window

Abstract

The invention provides a gray target tracking method based on a self-adaptive window and mainly aims at solving the problem of real-time size change of a self-adaptive target. The method comprises the steps of (1) determining a target template, (2) extracting characteristics of a mixed histogram, (3) initializing a particle point set, namely setting the sampled particle number N and initializing particle weights, (4) updating the state of the particle point set, (5) observing the particle point set, (6) estimating the state of the target, (7) tracking a window scaling, and (8) resampling. Under a particle filter tracking framework, the movement target template is determined, the state transition equation of the movement target is established, the particle set target window is initialized, the particle set of next moment is predicted, the weight of a likelihood function is calculated, a tie distance between high-weight particles and the center of the particle set is calculated, and the window size is adjusted linearly according to d. The self-adaptive adjustment of the tracking window is realized; compared with similar methods, the method has the advantages of simple calculation, high speed and good effect.

Description

technical field [0001] The invention relates to the technical field of video target tracking, in particular to a grayscale target tracking method based on an adaptive window. 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. In both algorithms, 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 noise will be mixed in while the target area is included in t...

AI Technical Summary

Problems solved by technology

When the target size becomes smaller, this method can get a better tracking effect, but when the target size becomes larger, since the similarity measure of the Bhattacharyya coefficient often reaches a local maximum in a smaller kernel window, the size of the tracking window is very small. Difficult to expand, often smaller and smaller

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