Target tracking method based on triple twin hash network learning

A target tracking and network learning technology, applied in neural learning methods, biological neural network models, instruments, etc., can solve the problems of large amount of parameter calculation and large memory space.

Active Publication Date: 2019-10-01
SOUTHWEST JIAOTONG UNIV
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a target tracking method based on triple twin hash network learning, which can effectively solve the problems of large memory space and large amount of parameter calculation caused by traditional deep learning directly using fully connected layer calculations. question

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  • Target tracking method based on triple twin hash network learning
  • Target tracking method based on triple twin hash network learning
  • Target tracking method based on triple twin hash network learning

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Embodiment Construction

[0030] The method of the present invention can be used in various occasions of visual target tracking, including military and civilian fields, military fields such as unmanned aerial vehicles, precision guidance, air early warning, etc., civil fields such as mobile robots, intelligent video monitoring of traction substations , intelligent transportation systems, etc.

[0031] Take the intelligent video surveillance of the traction substation as an example: the intelligent video surveillance of the traction substation includes many important automatic analysis tasks, such as intrusion detection, behavior analysis, abnormal alarm, etc., and the basis of these tasks must be able to achieve real-time and stable goals track. It can be realized by adopting the tracking method proposed by the present invention. Specifically, a triple twin hash network model needs to be constructed first. The network is composed of three parts: data input layer, convolutional feature extraction layer,...

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Abstract

The invention discloses a target tracking method based on triple twin hash network learning, and relates to the technical field of computer vision, target tracking and deep learning. According to themethod, firstly, a triple twin hash network is constructed, and the network is composed of a data input layer, a convolution feature extraction layer and a hash coding layer. In the initial training process of the network, a training data set and a random gradient descent back propagation algorithm are used for training the triple twinning Hash network, and after training is completed, the initialcapacity of target positioning can be obtained through the network. In the tracking process, firstly, an input image passes through a triple twin region recommendation network to obtain correspondingcandidate frames, then the candidate frames are input into a triple twin Hash network to be subjected to forward processing, the similarity between each candidate frame and a query sample is calculated, the candidate frame with the highest similarity is selected as a tracking target object, and therefore target tracking is achieved.

Description

technical field [0001] The invention relates to the technical fields of computer vision, target tracking and deep learning. Background technique [0002] Target tracking is a very popular research topic in the field of computer vision. Its research content is to automatically identify the target object to be tracked in the subsequent video sequence according to a given video clip, and obtain the continuous position, appearance and motion of the target. . Target tracking is widely used in military and civilian intelligent monitoring, human-computer interaction, traffic monitoring and other fields, and has strong practical value. Although this research topic has been studied for decades, it remains a challenging one. In real-world situations, target objects are easily disturbed by various factors, such as illumination changes, pose changes, target occlusion, etc., making developing a consistently robust target tracking system a very challenging problem. [0003] Over the pa...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46G06N3/04G06N3/08
CPCG06N3/08G06N3/084G06V10/454G06N3/045G06F18/22G06F18/214
Inventor 卢学民权伟周宁邹栋张卫华王晔郭少鹏刘跃平郑丹阳陈锦雄
Owner SOUTHWEST JIAOTONG UNIV
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