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Vehicle Tracking Method Based on Target Feature Sensitivity and Deep Learning

A deep learning and vehicle tracking technology, applied in the field of image processing, can solve problems such as tracking failure, and achieve the effects of low calculation, strong robustness, and high tracking accuracy

Active Publication Date: 2021-01-01
XIDIAN UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to address the deficiencies in the above-mentioned prior art, and propose a vehicle tracking method based on target feature sensitivity and deep learning, which is used to solve the problem of tracking failure caused by occlusion, illumination changes, etc. in the process of vehicle tracking

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  • Vehicle Tracking Method Based on Target Feature Sensitivity and Deep Learning
  • Vehicle Tracking Method Based on Target Feature Sensitivity and Deep Learning
  • Vehicle Tracking Method Based on Target Feature Sensitivity and Deep Learning

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Embodiment Construction

[0039] The technical solutions and effects of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0040] Refer to attached figure 1 , to further describe in detail the specific implementation steps of the present invention.

[0041] Step 1, construct a discriminative Siamese network.

[0042] Build two identical sub-networks, each sub-network has five layers, and its structure from left to right is: first convolutional layer → first downsampling layer → second convolutional layer → second downsampling layer → second Three convolutional layers; set the number of convolution kernels of the first, second, and third convolutional layers to 16, 32, and 1 in turn, and set the size to 3×3, 3×3, and 1×1 in turn; 1. The filter size of the second downsampling layer is set to 2×2.

[0043] The two sub-networks are set up and down in parallel and then connected with the cross-correlation layer XCorr to form a discriminative S...

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Abstract

The invention discloses a vehicle tracking method based on target feature sensitivity and deep learning, and mainly solves the problem of tracking failure caused by the fact that interferents similarto a vehicle target are easily judged as the vehicle target due to shielding, illumination change and the like in the vehicle tracking process in the prior art. The method comprises the following steps: constructing and training a discriminant connected network, extracting features through a trained public network model, selecting a filter which is more sensitive to a vehicle target, and trackingthe vehicle target by using the discriminant connected network and the selected sensitive filter. The method introduces sensitive filter bank selection and operation, and has the advantages of strongrobustness, good tracking effect, low calculation amount and easy realization.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a vehicle tracking method based on target feature sensitivity and deep learning in the technical field of target tracking. The invention can be used for tracking vehicles in unmanned driving, assisted driving and intelligent traffic. Background technique [0002] The task of vehicle tracking is to predict the size and position of the vehicle in the subsequent frames given the size and position of the vehicle in the initial frame of the video sequence. Tracking based on correlation filtering has been widely concerned because of its real-time performance. Tracking based on correlation filtering The tracking result of the previous frame is the training data to update the filter template. After the filter template is obtained, it is correlated with the features extracted from the current frame to obtain a response map. The position of the maximum response point on the ...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/246
CPCG06T2207/10016G06T2207/20024G06T2207/20081G06T2207/20084G06T7/246
Inventor 韩冰李凯杨铮朱考进郭凯珺
Owner XIDIAN UNIV
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