Semi-offline deep target tracking method based on deep learning

A target tracking and deep learning technology, applied in the field of semi-offline deep target tracking based on deep learning, can solve problems such as target recognition performance decline and target recognition failure, so as to reduce the probability of recognition failure, ensure recognition accuracy, and improve speed. Effect

Pending Publication Date: 2019-08-06
JIANGNAN UNIV
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AI Technical Summary

Problems solved by technology

[0003] In order to solve the problem that the deep learning-based online class tracker fails target recognition due to false samples in online updates, and frequent online updates lead to target recognition performance degradation, the present invention provides a

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  • Semi-offline deep target tracking method based on deep learning
  • Semi-offline deep target tracking method based on deep learning
  • Semi-offline deep target tracking method based on deep learning

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Embodiment Construction

[0024] Such as figure 1 As shown, the present invention is a semi-offline depth target tracking method based on deep learning, which includes the following steps.

[0025] S1: build the tracker network model, build the structure of the shared layer of the tracker network model based on MDNet;

[0026] The tracker network model of the semi-offline deep target tracker based on deep learning includes a shared layer and a specific domain layer; The weight of the domain layer allows it to adapt to specific target features, so as to achieve the purpose of identifying the target and background from the collected samples, and realize the determination of the target position.

[0027] The shared layer includes 3 consecutive convolutional layers and 2 consecutive fully connected layers connected in sequence; the network model receives pictures to be classified through the input layer, these pictures are pictures with 3 channels and a size of 107×107, and the input layer receives The p...

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Abstract

According to the semi-offline deep target tracking method based on deep learning provided by the invention, when the semi-offline deep target tracking method is used for target tracking, the problem of target tracking failure caused by false samples in online updating can be avoided, and the target tracking recognition performance can be improved. The method comprises the following steps: S1, constructing a tracker network model, and constructing a structure of a shared layer of the tracker network model based on an MDNet, wherein the specific domain layer is included and comprises a Dropout layer, a full connection layer and a classification function which are connected in sequence, and the feature map output by the last full connection layer in the sharing layer is input into a specificdomain layer after being processed by an activation function; s2, selecting a training set to obtain a trained tracker network model; and S3, obtaining a to-be-tracked feature sequence from the video,inputting the to-be-tracked feature sequence into the trained tracker network model, carrying out subsequent target tracking operation, training the specific domain layer through a sample collected on a first frame in the to-be-tracked feature sequence before the target tracking operation is started, and defining the network weight of the specific domain layer.

Description

technical field [0001] The invention relates to the technical field of target recognition, in particular to a semi-offline deep target tracking method based on deep learning. Background technique [0002] The target tracking technology in computer vision technology has a wide range of applications in many fields such as intelligent monitoring, human-computer interaction, unmanned driving, virtual reality and even military affairs. Based on the deep learning target tracking technology, the tracker is divided into online tracking and offline tracking according to whether to update the weight of the network model. For online trackers, the weight of the network model is more, in order to adapt to the change of the target in the tracking process, it is often necessary to adjust the weight of the network online. Among them, the MDNet (MultiDomain Network) tracker is a typical online tracker , the core idea is: first learn the general representation of the target in different type...

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Application Information

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IPC IPC(8): G06T7/246
CPCG06T7/246G06T2207/10016G06T2207/20081G06T2207/20084
Inventor 陈秀宏孙海宇
Owner JIANGNAN UNIV
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