Target tracking method based on transfer learning regression network

A transfer learning and network technology, applied in the field of computer vision, can solve problems such as inaccurate target positioning

Active Publication Date: 2018-09-14
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to provide a target tracking method based on migration learning regressi

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  • Target tracking method based on transfer learning regression network
  • Target tracking method based on transfer learning regression network

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[0028] The method of the present invention can be used in various situations of target tracking, such as intelligent video analysis, automatic human-computer interaction, traffic video monitoring, unmanned vehicle driving, biological group analysis, wild animal motion analysis, detection of moving objects at intersections, and fluid surface velocity measurement Wait.

[0029] Take intelligent video analysis as an example: Intelligent video analysis includes many important automatic analysis tasks, such as behavior analysis, abnormal alarm, video compression, etc. The basis of these tasks is the ability to perform stable target tracking. It can be realized by the tracking method proposed by the present invention. Specifically, a location regression network based on migration learning is first established, such as figure 1 As shown, a variety of transformations are performed on the target and image, and then the corresponding training data set is synthesized. The network training i...

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Abstract

The invention provides a target tracking method based on a transfer learning regression network, which relates to the technical field of computer vision. A to-be-tracked target object is selected anddetermined from an initial image; a target position regression network is constructed based on block prediction; generation of a training data set for tracking and network training are carried out; image input is carried out, in a real-time processing condition, a video image acquired by a camera and saved in a storage area is extracted as a to-be-tracked input image; and target positioning is carried out, the acquired image is inputted to the position regression network, after network forward processing, a network output layer obtains 8*8*8 relative position data. Network updating is carriedout, according to the obtained target position, 8*8*8 relative positions between 8*8 image blocks divided by the whole image and the target are calculated, and together with the current input image, agroup of training data can be formed.

Description

technical field [0001] The invention relates to the fields of computer vision, computer graphics, machine intelligence and system technology. Background technique [0002] Visual object tracking is an important research topic in the field of computer vision. Its main task is to obtain the continuous position, appearance and motion information of the object, and then provide the basis for further semantic analysis (such as behavior recognition, scene understanding, etc.). Target tracking research is widely used in intelligent monitoring, human-computer interaction, automatic control systems and other fields, and has strong practical value. At present, target tracking methods mainly include classical target tracking methods and deep learning target tracking methods. [0003] The classic target tracking methods are mainly divided into two categories: Generative Methods and Discriminative Methods. The generative method assumes that the target can be expressed through some kind...

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Application Information

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IPC IPC(8): G06T7/246
CPCG06T2207/10016G06T2207/20021G06T2207/20081G06T2207/20084G06T7/248
Inventor 权伟李天瑞江永全何武刘跃平卢学民王晔贾成君陈锦雄
Owner SOUTHWEST JIAOTONG UNIV
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