Adaptive tracking of correlated filtered video based on artificially combined depth features

An adaptive tracking and deep feature technology, applied in the field of computer vision, can solve the problems of increasing the computational burden and poor real-time performance, and achieve the effect of enhancing the representation ability, improving the stability, and increasing the background information.

Inactive Publication Date: 2019-03-12
NANJING UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

[0005] However, the traditional discriminative methods have an important defect, that is, in order to enhance the discriminative ability, a large number of tr

Method used

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  • Adaptive tracking of correlated filtered video based on artificially combined depth features
  • Adaptive tracking of correlated filtered video based on artificially combined depth features
  • Adaptive tracking of correlated filtered video based on artificially combined depth features

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Experimental program
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Embodiment

[0068] Such as figure 1 As shown, a correlation filtering video adaptive tracking method combining artificial and depth features, including the following steps:

[0069] Step 1: Deep Neural Network Pre-training

[0070] Pre-training of deep neural networks on ImageNet, a large-scale dataset with class labels.

[0071] Step 2: Deep Convolutional Feature Extraction

[0072] (1) At the t-th frame of the image, according to the target position and scale, take the position as the center of the candidate area, and the scale as the size of the candidate area, crop the image to obtain the target candidate block z t ;

[0073] (2) The candidate block z t Input to the pre-trained deep neural network in step 1 for forward calculation, and extract the convolutional features of the Conv3 layer, Conv4 layer and Conv5 layer respectively.

[0074] Step 3: Manual Feature Extraction

[0075] (1) At the t-th frame of the image, according to the target position and scale, take the position ...

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Abstract

The invention discloses a correlation filtering video adaptive tracking method combined artificially with depth features, which comprises the following steps: pre-training a depth neural network; Depth convolution feature extraction; Manual feature extraction; Multi-feature fusion computation; Scale Adaptive Target Detection and Response Computation; Correlation filter model updating; Output current frame tracking results. As that depth convolution feature are adopted, The gradient direction histogram and color name feature are fused to model the appearance of the object, the background response is suppressed by the spatial context information of the object, the discriminant ability of the model is enhanced by the adaptive scale estimation method, and the computational complexity is reduced by the fast Fourier transform in the frequency domain. The invention improves the robustness of tracking under complex scenes, and can be widely applied to the fields of video surveillance, human-computer interaction, robot technology, road scene understanding and the like.

Description

technical field [0001] The invention relates to the field of computer vision, in particular to a correlation filtering video adaptive tracking method combining artificial and depth features. Background technique [0002] Target tracking is an important research content in the field of computer vision. Target tracking is mainly based on the position of the target in the first frame or the first few frames in the video to estimate the position trajectory of the target in the subsequent sequence. At present, the methods in the field of target tracking technology are mainly divided into two categories: [0003] (1) Generative method: This method mainly uses the generative model to describe the appearance characteristics of the target, and finds the region most similar to the target appearance in the subsequent sequence, that is, minimizes the reconstruction error by searching for candidate targets. More representative algorithms include sparse coding, Kalman filter, particle fi...

Claims

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Application Information

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IPC IPC(8): G06T7/246G06T7/262G06T7/269G06T7/207G06N3/04
CPCG06T7/207G06T7/246G06T7/262G06T7/269G06T2207/20081G06T2207/10016G06N3/045
Inventor 肖亮张乐意
Owner NANJING UNIV OF SCI & TECH
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