A video tracking algorithm based on multi-template and adaptive feature selection

Abstract

The invention belongs to the field of computer graphics and image processing, in particular to a video tracking algorithm of multi-template and adaptive feature selection. The invention aims at the single feature problem and the frequent template updating problem of the previous algorithm. A feature fusion method combining convolution feature and manual feature is proposed, and the feature channels are weighted by dimension reduction and entropy value. At the same time, the multi-template is trained to get multi-response map in each frame. After fusion, the final target position and scale arefound, and the template is updated according to the threshold value. The method of the invention has the following beneficial effects: 1. under the condition of ensuring real-time processing, the adaptability of tracking under complex scenes is effectively improved; 2. The accuracy of single target tracking is improved.

Description

technical field [0001] The invention belongs to the field of computer graphics and image processing, and relates to a video tracking algorithm for multi-template and self-adaptive feature selection. Background technique [0002] With the advent of the AI ​​era, the new technology of image graphics ushers in a new peak. Single target tracking is an important research direction in the field of machine vision. It is widely used to find the target of interest in the first frame in each frame of a video sequence. In video surveillance, human-computer interaction, medical image processing and other fields. Although some excellent algorithms have emerged in recent years, single-target video tracking is still a challenging task. Various interference factors such as deformation, occlusion, background clutter, and scale changes restrict the improvement of tracking effects. [0003] Existing algorithms such as CN, KCF, HCF and other algorithms use a single feature to express weakly; S...

AI Technical Summary

Problems solved by technology

[0003] Existing algorithms such as CN, KCF, HCF and other algorithms use a single feature to express weakly; SAMF algorithm combines HOG features, CN features and gray features, but the fusion of features is simply superposition, and does not consider The importance degree between feature channels and the redundancy problem of feature superposition

At the same time, there are also methods such as MOSSE that update each frame when the template is updated, which is easy to cause tracker drift.

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