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Target tracking method based on hierarchical feature response fusion

A target tracking and feature response technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as unsatisfactory tracking effect and low accuracy

Active Publication Date: 2020-04-14
HUAQIAO UNIVERSITY +1
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  • Description
  • Claims
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Problems solved by technology

[0005] The technical problem to be solved by the present invention is to provide a target tracking method through layered feature response fusion. By changing the conditions of layered feature adaptive fusion and model update, it overcomes the low accuracy of correlation filter tracking and the tracking Problems with unsatisfactory results

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  • Target tracking method based on hierarchical feature response fusion
  • Target tracking method based on hierarchical feature response fusion
  • Target tracking method based on hierarchical feature response fusion

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Embodiment Construction

[0081] Please refer to figure 1 and figure 2 , a target tracking method through layered feature response fusion provided by the embodiment of this specification may include the following steps:

[0082] Step 10, initialize the parameters, the parameters include: hierarchical correlation filter {W t l |l=3,4,5}, filter regular term weight factors λ and λ1, tracking model learning factor η, scale series S, scale increment factor θ, weight update parameter τ, fixed threshold δ, weight factor α;

[0083] Step 20, extracting the layered features of the target image and performing response value fusion to obtain a position model; specifically, the following steps may be included:

[0084] Step 201, when the tth frame of the video sequence, with the target point P(x t ,y t ) as the center to obtain an area of ​​a set size as the target sample, put it into the convolutional neural network, extract the 3, 4, and 5-layer convolution features, and obtain the feature images of the 3...

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Abstract

The invention discloses a target tracking method based on hierarchical feature response fusion, and relates to the field of computer vision target tracking. The method comprises the steps of 10 initializing parameters; 20 extracting hierarchical features of the target image to perform response value fusion to obtain a position model; 30 training the maximum scale response value of a scale correlation filter to obtain a scale model; 40 when a fusion response value obtained after the response values are fused in the step 20 is smaller than or equal to a set threshold value, re-detecting the target image to obtain a candidate region, and returning to the step 20; when the fusion response value is greater than a set threshold, updating the position model and the scale model, and then enteringthe step 50; and 50 applying the updated position model and scale model to next frame tracking, and returning to the step 40. According to the method provided by the invention, the conditions of hierarchical feature adaptive fusion and model updating are changed; the tracking accuracy of the related filter is improved; and the tracking effect is more ideal.

Description

technical field [0001] The invention relates to the field of computer vision target tracking, in particular to a target tracking method through hierarchical feature response fusion. Background technique [0002] Visual object tracking is a basic task in the field of computer vision, and it is widely used in autonomous driving, robotics, video surveillance, and human-computer interaction. Although given the initial frame of the object, how to predict the position of the object in consecutive video frames using an efficient method is still a difficult problem. Although great development has been achieved in recent years, it still faces challenges from many external factors. For example, the target usually experiences some disturbances, such as occlusion, background blur, fast motion, illumination change, deformation, scale change and out of view, and these disturbances will affect the accuracy and robustness of target tracking. [0003] At present, the two major directions o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/246G06K9/62
CPCG06T7/246G06T2207/10016G06T2207/20081G06T2207/20084G06T2207/20004G06F18/25Y02T10/40
Inventor 柳培忠邓建华张万程杜永兆陈智吴奕红杨建兰
Owner HUAQIAO UNIVERSITY
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