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Tracking algorithm based on spatio-temporal context fusion multi-feature and scale filtering

A spatiotemporal context, tracking algorithm technology, applied in computing, image data processing, instruments, etc., can solve problems such as scale, deformation and occlusion, and achieve the effect of increasing accuracy, simple implementation, and reducing the introduction of noise.

Inactive Publication Date: 2018-02-09
YANSHAN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to solve the common problems of scale, deformation and occlusion in current tracking, and propose a tracking algorithm based on spatio-temporal context fusion of multiple features and scale filtering

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  • Tracking algorithm based on spatio-temporal context fusion multi-feature and scale filtering
  • Tracking algorithm based on spatio-temporal context fusion multi-feature and scale filtering
  • Tracking algorithm based on spatio-temporal context fusion multi-feature and scale filtering

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Embodiment Construction

[0059] The present invention will be further described below in conjunction with accompanying drawing:

[0060] Such as figure 1 Shown, method of the present invention comprises the following steps:

[0061] Step 1: Obtain a video or picture sequence, obtain the target position information and scale information in the first frame image, and use it as a template image to prepare for subsequent target tracking.

[0062] Step 2, parameter initialization, including the public parameters target center position, target size and target surrounding area and non-public parameters scale model, adaptive appearance model, and position model three independent experience given, whether it is public parameters or non-public parameters The values ​​assigned are all empirical values, in order to make the tracking algorithm have better generalization ability, but it can be changed according to actual needs to achieve better tracking effect;

[0063] Step 3, multi-feature extraction, the proce...

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Abstract

The invention provides a tracking algorithm based on spatio-temporal context fusion multi-feature and scale filtering. Target position information and size information of a first frame of image are obtained through a video or image sequence; parameter initialization is performed; multi-feature extraction is performed; double-step preprocessing operation is performed, and operation of the cosine window is performed in a feature target area so that the edge effect caused by Fourier transform can be reduced; a filtering template and a scale filtering template are obtained through two times of Fourier transform; a position filtering template and the scale filtering template under the time domain space are obtained through two times of inverse Fourier transform, and the corresponding maximum value, i.e. the target area, is solved; finally the position filtering model, the scale filtering model, an adaptive appearance model and spatio-temporal context information are updated through a new frame; and the process returns to the feature extraction part and target tracking is performed through cyclic operation until the end of the process. According to the method, the tracking accuracy can be enhanced, the appearance change and the scale change of the target in the tracking process can be better adapted and the noise caused by the change of the environment in the tracking process can bereduced.

Description

technical field [0001] The invention relates to the field of machine vision target tracking, in particular to an improved tracking algorithm based on spatio-temporal context fusion of multiple features and scale filtering. Background technique [0002] Object tracking is an important research direction of computer vision, which has a wide range of applications in video surveillance, human-computer interaction, motion analysis, and activity recognition. The main challenges facing object tracking are: appearance changes, illumination changes, pose changes, and occlusions. At present, the common target tracking algorithms are mainly divided into two types: the generation method continuously searches for the region most similar to the target, and this type of method is either based on template matching or based on a subspace model. Discriminative methods aim to distinguish the object from the background, which is to turn the tracking problem into a binary classification problem...

Claims

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Application Information

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IPC IPC(8): G06T7/246
CPCG06T7/251G06T2207/10016
Inventor 胡硕韩江龙孙翔赵银妹
Owner YANSHAN UNIV
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