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A Visual Object Tracking Method Based on Deep Residual Network Features

A technology of target tracking and network features, which is applied in the field of visual target tracking based on deep residual network features, can solve problems such as restricting the application of tracking algorithms, and achieve the effects of easy promotion and use, simple steps, and good use effects

Active Publication Date: 2021-07-16
CHANGAN UNIV +1
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Problems solved by technology

The discriminative model algorithm trains a classifier to distinguish the target from the background, and selects the candidate sample with the highest confidence as the prediction result. Due to the limitation of the accuracy and speed of the tracking algorithm, the application of the tracking algorithm in the actual scene is still restricted.

Method used

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  • A Visual Object Tracking Method Based on Deep Residual Network Features

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Embodiment Construction

[0037] Such as figure 1 Shown, a kind of visual target tracking method based on depth residual network feature of the present invention, comprises the following steps:

[0038] Step 1. Select the feature layer of the deep residual network and calculate the weight corresponding to each feature layer: In the marked public data set, use each layer in the deep residual network ResNet-N to separately classify the marked public data set Extract features from the video, calculate the tracking overlap rate, select the layer with the top three tracking overlap rates to construct the first training sample, and train the convolutional neural network (CNN) 1 , Convolutional Neural Network CNN 1 by the input layer I 1 , convolutional layer C 1 , pooling layer P 1 , convolutional layer C 2 , pooling layer P 2 , convolutional layer C 3 , pooling layer P 3 , fully connected layer F and output layer O 1 Composed, the image sequence to be tracked passes through the convolutional neural...

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Abstract

The invention discloses a visual target tracking method based on deep residual network features, comprising steps: 1. Selecting the feature layer of the deep residual network and calculating the weight; 2. Extracting the feature of the actual input image of the first frame; 3. Constructing The response and initial position filter of the features of the actual input image in the first frame; 4. Scale sampling and fHOG feature extraction of the actual input image in the first frame; 5. Construction of the initial scale filter; 6. Features of the actual input image in the second frame Extraction; 7. Position filtering; 8. Weighting of position filter response map and target positioning; 9. Scale sampling of target image and fHOG feature extraction; 10. Scale filtering and scale estimation of target feature vector; 11. Filter update; 12. Input the next frame of actual input image, take the next frame of actual input image as the second frame of actual input image, and then repeat step 6. The invention has high tracking accuracy and success rate, adapts to the change of target scale, and realizes robust tracking of the target.

Description

technical field [0001] The invention belongs to the technical field of target tracking, and in particular relates to a visual target tracking method based on deep residual network features. Background technique [0002] The application of artificial intelligence is very extensive, covering many technical fields, mainly including computer vision, natural language processing, cognition and reasoning, robotics, game and ethics, machine learning, etc. Vision is the most important source of information for the human brain, and it is also the door to enter the palace of artificial intelligence. About 70% of human cerebral cortex activity is processing visual related information. Computer vision uses images (videos) as input to study image information organization, object and scene recognition, etc., and then explain events, so as to realize the expression and understanding of the environment. [0003] As one of the basic problems in the field of computer vision, object tracking ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/246G06N3/04
Inventor 马素刚赵祥模侯志强王忠民惠飞
Owner CHANGAN UNIV
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