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Video tracking method based on rank learning

A video tracking and ranking learning technology, applied in the field of video tracking based on ranking learning, can solve problems such as drift and lost tracked targets

Inactive Publication Date: 2014-06-25
ZHEJIANG UNIV
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Most detection-based video tracking algorithms can handle object changes in some real scenes, but they all have drift problems to varying degrees, resulting in the loss of the tracked target

Method used

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  • Video tracking method based on rank learning
  • Video tracking method based on rank learning
  • Video tracking method based on rank learning

Examples

Experimental program
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Embodiment 1

[0114] A video tracking method based on ranking learning, comprising the following steps:

[0115] 1) Read in the initial image frame and initialize the target position parameters in is the coordinates of the upper left corner pixel of the target, w and h Indicates the width and height of the target.

[0116] 2) Extract target image block sample set and background sample set

[0117] X t 1 = { x ′ : | | l s ( x ′ ) - l s * | | ≤ α , s = 1 , t - Δt , . . . , t }

[0118] ...

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Abstract

The invention discloses a video tracking method based on rank learning. The method comprises the steps of firstly compressing multi-scale image features by using a sparse measurement matrix based on a compressed sensing theory, secondly using a Median-Flow tracking algorithm as a predictor to obtain the rough position of a target and constructing a training data set for an RV-SVM algorithm, and finally sorting training samples and taking the RV-SVM algorithm as a binary classifier to separate the target and a background to achieve the purpose of video tracking. The training process of the RV-SVM algorithm is a linear programming problem, the training time of online learning is reduced, and the efficiency of a tracking system is improved. Through the combination of multi-scale image compression feature extraction, the Median-Flow tracking algorithm and the RV-SVM algorithm, problems of target scale change, partial occlusion, 3D rotation, posture change, target fast movement and the like in a video tracking process can be effectively processed.

Description

technical field [0001] The invention belongs to the field of computer vision and pattern recognition, in particular to a video tracking method based on ranking learning. Background technique [0002] Video tracking is a key research topic in the field of computer vision, and has broad application prospects in intelligent video surveillance, augmented reality, human-computer interaction, gesture recognition, and automatic driving. In the past two decades, although researchers at home and abroad have proposed a lot of tracking algorithms, it is still a very challenging topic, because efficient video tracking algorithms need to deal with target scale changes, illumination changes, Partial occlusion, camera rotation, object deformation, etc. [0003] According to the different methods used to model the target performance, tracking algorithms can be divided into two categories: target tracking algorithms based on generative models and target tracking algorithms based on discrimi...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06K9/62
Inventor 于慧敏曾雄
Owner ZHEJIANG UNIV
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