Information tracking method based on convolutional neural network
A convolutional neural network and neural network technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as insufficient, uncertain tracking of target frame motion information, and insufficient use of video temporality. To achieve the effect of accurate tracking, improve accuracy, and improve accuracy
Pending Publication Date: 2022-04-29
广州新华学院
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Abstract
The invention provides an information tracking method based on a convolutional neural network. The method comprises the following steps: constructing a correlation filtering convolutional neural network in combination with correlation filtering and a convolutional neural network; constructing a time flow convolutional neural network and a spatial flow convolutional neural network on the basis; the three parts are constructed to form a deep network in a jumping connection mode; training the deep network until the three models are all converged; respectively extracting image block feature information in the current frame and a motion change feature information set of a target between frames in a plurality of time sequences through a time stream convolutional neural network and a spatial stream convolutional neural network; fusing the image block feature information and the motion change feature information weight, constructing a full-connection neural network, and obtaining prediction information of the current frame target; and fusing all models by using a Bagging algorithm to determine final prediction information of the current frame, constructing a time network and a space network on the basis of a related filtering network, further capturing time information and space information of a target, and improving the accuracy of the algorithm.
Application Domain
Image enhancementImage analysis +2
Technology Topic
Space NetworkConvolution +4
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