An Object Tracking Method Based on Transfer Learning Regression Network

A transfer learning, network technology, applied in the field of computer vision, can solve the problem of inaccurate target positioning

Active Publication Date: 2021-08-17
SOUTHWEST JIAOTONG UNIV
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a target tracking method based on migration learning regression network, the problem of inaccurate training data and target positioning when deep neural network is used for tracking

Method used

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  • An Object Tracking Method Based on Transfer Learning Regression Network
  • An Object Tracking Method Based on Transfer Learning Regression Network

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Embodiment Construction

[0028] The method of the present invention can be used in various occasions of target tracking, such as intelligent video analysis, automatic human-computer interaction, traffic video monitoring, unmanned vehicle driving, biological group analysis, field animal movement analysis, intersection moving object detection, and fluid surface speed 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., and the basis of these tasks is the ability to perform stable target tracking. It can be realized by adopting the tracking method proposed by the present invention. Specifically, firstly, a position regression network based on migration learning is established, such as figure 1 As shown, multiple transformations are performed on the target and the image, and then the corresponding training data set is synthesized. The network...

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Abstract

The invention provides a target tracking method based on a transfer learning regression network, and relates to the technical field of computer vision. Select and determine the target object to be tracked from the initial image; build a target position regression network based on block prediction; track-oriented training data set generation and network training; image input, in the case of real-time processing, extract and save through the camera The video image in the storage area is used as the input image to be tracked; for target positioning, the obtained image is input into the position regression network, and after the forward processing of the network, the network output layer will obtain 8×8×8 relative position data. The network update calculates 8×8×8 relative positions between the 8×8 image blocks divided by the whole image and the target according to the obtained target position, and forms a set of training data together with the current input image.

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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Patent Type & Authority Patents(China)
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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