Target tracking method based on local features and scale pool

A target tracking, local feature technology, applied in computer parts, image data processing, instruments, etc., can solve the problems of low robustness and poor accuracy of target tracking algorithms

Active Publication Date: 2019-08-06
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0005] In view of the above-mentioned research problems, the purpose of the present invention is to provide a target tracking method based on local features and scale pools to solve t

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  • Target tracking method based on local features and scale pool
  • Target tracking method based on local features and scale pool
  • Target tracking method based on local features and scale pool

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Embodiment Construction

[0124] The present invention will be further described below in conjunction with the drawings and specific embodiments.

[0125] The present invention is based on the KCF framework. The first frame of image acquires the target according to the initial information, and the classifier is trained based on two features of the target to obtain the target model of the corresponding feature and the classifier regression coefficients are reinitialized. The second frame of image uses the scale pool to obtain different scales. And extract the 31-dimensional FHOG feature as feature one, and the one-dimensional gray feature, the one-dimensional de-averaged gray feature, and the one-dimensional local binary mode LBP feature fusion into the three-dimensional fusion feature as feature two; then based on the feature The initialized target model and classifier regression coefficients corresponding to feature one and feature two obtain the multi-layer kernel correlation filter response map correspo...

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Abstract

The invention discloses a target tracking method based on local features and a scale pool, belongs to the technical field of grayscale image target tracking, and solves the problem of poor target tracking algorithm accuracy in complex environments such as illumination change, scale change and background interference. The method comprises the steps of a first frame image obtaining a target according to initial information, training a classifier on the basis of two features of the target, obtaining and then initializing a target model and a classifier regression coefficient, a second frame imageobtaining targets of different scales through a scale pool, and extracting a first feature and a second feature; based on the initialized target model and the classifier regression coefficient, obtaining a multi-layer kernel correlation filtering response graph of the two features; and performing linear interpolation to the same size, performing weighted fusion to obtain a multi-layer kernel correlation filtering response graph, further obtaining a prediction position and a prediction scale of the target, namely finishing one-time target tracking, and if the tracking is not finished, realizing tracking from the second frame of image to the third frame of image until the tracking is circulated to the last frame of image. The method is used for target tracking.

Description

Technical field [0001] A target tracking method based on local features and scale pool, used for target tracking, belongs to the field of gray image target tracking technology. Background technique [0002] Target tracking has very important significance and value in the field of computer vision. It has wide applications in many fields, such as intelligent video surveillance, medical care, human-computer interaction and other civilian fields. In the military, it can quickly and accurately target enemy moving targets. Search and tracking etc. Target tracking is mainly divided into generative model and discriminative model. The generative model establishes the target mathematical model to complete the matching between the candidate target and the target model, and takes the most similar candidate area as the prediction target. The discriminant model is composed of a training set composed of positive samples belonging to the target and negative samples belonging to the background ...

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Application Information

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IPC IPC(8): G06T7/246G06T7/269G06K9/62
CPCG06T7/246G06T7/269G06T2207/20056G06T2207/20081G06T2207/10016G06F18/253G06F18/214
Inventor 张文超彭真明李美惠龙鸿峰彭凌冰秦飞义张鹏飞曹兆洋孔轩张兰丹程晓彬
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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