Unmanned plane target tracking method based on hierarchical model

Abstract

The invention discloses an unmanned plane target tracking method based on a hierarchical model, and belongs to the field of unmanned plane remote sensing image processing. The unmanned plane target tracking method based on a hierarchical model includes the following steps: 1) establishing characteristic expression of an initial frame target; 2) establishing a coarse tracking model taking mean value drifting as the basis; and 3) establishing a precise tracking model taking improved particle swarm optimization as the basis. The unmanned plane target tracking method based on a hierarchical model effectively overcomes the problem that the imaging particularity of an unmanned plane causes high difficulty and low accuracy in video tracking, and the video tracking is easy to lose by introducing hierarchical model combined with mean value drifting and improved particle swarm optimization, and effectively improves the accuracy of target tracking.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a method for tracking an unmanned aerial vehicle target based on a layered model. Background technique [0002] Unmanned aerial vehicles (UAVs) are used more and more widely in both civilian and military fields, so their development has attracted the attention of various countries. Among many research directions, visual capabilities will become the focus of future development and competition of UAVs. UAV visual tracking, as the main force of UAV vision, has also become a key technology. According to public information, as early as 2005, the computer vision laboratory of the University of Central Florida in the United States developed the COCOA system based on MATLAB, which is mainly used for target detection and tracking processing of low-altitude aerial video images of UAVs. At present, domestic target tracking methods can be roughly divided ...

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Problems solved by technology

The advantage is that the search speed is fast, the search efficiency is high, and the algorithm is simple; the disadvantage is that it does not handle discrete optimization problems well, and it is easy to fall into local optimum

[0006] Among them, the particle filter strategy and the particle swarm optimization search strategy both need a certain number of particles. When the number of particles is more, the accuracy rate will be higher, but the search time will be correspondingly increased; when the number of particles is small, the search time will be reduced. Small, but some areas may not be searchable, because the particle swarm optimization strategy uses the iterative idea, the impact on the accuracy is small, but

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