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Long-time target tracking method and system based on layered convolution feature

A target tracking, long-term technology, used in image analysis, instruments, biological neural network models, etc., can solve problems such as poor tracking accuracy, low resolution, motion blur, etc., to improve tracking speed, improve accuracy, and ensure reliability. sexual effect

Active Publication Date: 2018-12-25
济南市中未来产业发展有限公司
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Problems solved by technology

[0003] The target tracking process involves a series of challenges such as illumination changes, scale changes, in-plane rotation, out-of-plane rotation, occlusion, deformation, motion blur, fast motion, background spots, and low resolution. The existing "correlation filter" class target tracking methods Although the tracking speed is fast, the tracking accuracy is not good

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  • Long-time target tracking method and system based on layered convolution feature
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[0050] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0051]It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinations thereof.

[0052] In the specific implementation example of the present disclosure, the long-term object tracking method LOT...

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Abstract

The invention discloses a long-time target tracking method and a system based on layered convolution characteristics. The invention adopts a pre-trained depth convolution neural network to extract convolution characteristics of each layer for each frame of video data; according to the model of correlation filter, the response of convolution layer is calculated and weighted, and the maximum value of the final response is taken as the target center of the current frame; in the process of frame-by-frame updating, the threshold value is set, and the correlation filter model is updated only when the tracking response value is greater than the threshold value, otherwise, the correlation filter model of the previous frame is adopted, and at the same time, when the tracking response value is lowerthan the set threshold value, the stochastic fern algorithm is used to re-detect the target. The invention adopts depth learning for feature extraction, and improves the precision of target tracking;the modified model updating method reduces the computational redundancy in the whole tracking process and improves the tracking speed; a random fern algorithm is used to re-detect the target in bad conditions such as target occlusion, which ensures the reliability of the actual tracking results.

Description

technical field [0001] The present disclosure relates to the technical field of video target tracking, in particular to a long-term target tracking method and system based on hierarchical convolution features. Background technique [0002] Target tracking is of great significance to the development of robotics, unmanned aerial vehicle, automatic driving, navigation and guidance and other fields. For example, in the process of human-computer interaction, the camera continuously tracks the human body's behavior, and through a series of analysis and processing, the robot can understand the human body's posture, movement, and gestures, so as to better realize the friendly communication between humans and machines; During the target tracking process of the UAV, the visual information of the target is continuously obtained and transmitted to the ground control station, and the video image sequence is analyzed through an algorithm to obtain the real-time position information of the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/246G06N3/04
CPCG06T7/246G06N3/045
Inventor 刘允刚生晓晓梁会军李峰忠
Owner 济南市中未来产业发展有限公司
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