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
0 Cites 0 Cited by

AI-Extracted Technical Summary

Problems solved by technology

[0004] In order to solve the above technical problems, the present invention provides an information tracking method based on a convolutional neural network to solve the problem that the existing information tracking method d...
View more

Method used

The present invention makes the present invention need not carry out time-consuming off-line training by correlative filter convolutional neural network, builds time network and space network on the basis of correlative filter network, captures the time information and the space information of target further, improves The accuracy of the algorithm; at the s...
View more

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

Image

  • Information tracking method based on convolutional neural network

Examples

  • Experimental program(1)

Example Embodiment

[0022] The invention will be further explained with reference to the following drawings and embodiments.
[0023] An information tracking method based on convolutional neural network, such as Figure 1 Shown in, including the following operation steps:
[0024] Combining correlation filtering and convolution neural network to construct correlation filtering convolution neural network, in which correlation filtering network is a layer of convolution neural network formed by correlation filtering algorithm;
[0025] Building a time flow convolution neural network and a space flow convolution neural network on the basis of the correlation filtering convolution neural network built in step one;
[0026] The correlation filtering convolution neural network, the time flow convolution neural network and the space flow convolution neural network are constructed to form a deep network by using the jumping connection mode.
[0027]The deep network is trained until the three models of correlation filtering convolution neural network, time flow convolution neural network and space flow convolution neural network all converge; Comprises the following steps of: taking a frame of a target position as an input, extracting a training block with the target position as the center, obtaining feature extraction and response mapping, wherein the size of the training block obtained in the first frame is five times of the maximum width and height of the target, adopting a deep convolution network for feature extraction, and simultaneously initializing the parameters of a correlation filtering convolution neural network, a time convolution neural network and a spatial convolution neural network to zero-mean Gaussian distribution, The training block of the first frame passes through the deep convolution network to get the feature maps of the third, fourth and fifth layers, and then the feature maps of these three layers are sent into the deep network connected by the correlation filtering convolution neural network, the time convolution neural network and the space convolution neural network, and the three models are trained at the same time until they converge.
[0028] The obtained time-flow convolution neural network and space-flow convolution neural network respectively extract the image block feature information in the current frame and the motion change feature information set of the target between frames in the multi-segment time series, and select the weight of each identical feature information in the motion change feature information set of the target between frames in the multi-segment time series; Specifically, extracting the feature information of the image blocks in the current frame through the spatial flow convolutional neural network specifically includes: determining the target area in the previous frame of the current frame, sampling N image blocks in the current frame through Gaussian sampling, and taking them as the input of the spatial flow convolutional neural network; Complete the zeroing around n image blocks by Numpy function, and finally output the image features, in which the spatial flow convolution neural network includes three convolution layers and two pooling layers, and the number of zeroing around n image blocks by three convolution layers needs to be changed; The method comprises the following steps of: extracting motion change characteristic information of a target between frames in a plurality of time sequence ranges through a time-flow convolution neural network, and specifically, inputting m images into the time-flow convolution neural network; Then, an area of the target area in the previous frame which is concentrically and proportionally enlarged at least twice is taken, and an image is intercepted in the current frame; At last, the zeroing around m image blocks is completed by Numpy function, and finally the image features are output, wherein the time-flow convolution neural network includes eight convolution layers and five pooling layers, and it is also necessary to change the number of zeroing around m images by eight convolution layers;
[0029] The obtained image block feature information and motion change feature information weights are fused, a fully connected neural network is constructed according to the fused information, and the image blocks meeting the requirements are obtained, and then the frame regression operation is performed on the obtained image blocks to obtain the prediction information of the current frame target, wherein, the first fully connected layer and the second fully connected layer are cascaded in sequence, and B fully connected layer branches are deployed in parallel behind the second fully connected layer, and each fully connected layer branch serves as a third fully connected layer;
[0030] The fused feature information is used as the input of the fully connected neural network, and a two-dimensional vector is output through the calculation of the fully connected neural network. Among the image blocks of the spatial flow convolution neural network, the image block with the highest similarity score to the target is selected as the image block that meets the requirements.
[0031] The prediction information of the current frame and the determined prediction information of the previous frame are input into the corresponding trained correlation filtering convolution neural network, time flow convolution neural network and spatial flow convolution neural network models to obtain response mapping, and the Bagging algorithm is used to fuse all models to determine the final prediction information of the current frame.
[0032] According to the invention, through the convolution neural network of the correlation filter, the time-consuming offline training is not needed, and the time network and the space network are built on the basis of the correlation filter network, so that the time information and the space information of the target are further captured, and the accuracy of the algorithm is improved; At the same time, the convolution neural network is used to extract the motion change information of the tracking target between frames, which makes full use of the timeliness of video, reduces the influence of target occlusion and background noise, improves the accuracy of target position prediction information, and makes the tracking effect more accurate.
[0033] In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside" and "outside" are based on the directions or positional relationships shown in the drawings, only for the convenience of describing the present invention and simplifying the description, and are not indicated or implied.
[0034] The above are only examples of the present invention, which do not limit the patent scope of the present invention. Any equivalent structure or equivalent process changes made by using the contents of the present specification and drawings, or directly or indirectly applied in other related technical fields, are equally included in the patent protection scope of the present invention.

PUM

no PUM

Description & Claims & Application Information

We can also present the details of the Description, Claims and Application information to help users get a comprehensive understanding of the technical details of the patent, such as background art, summary of invention, brief description of drawings, description of embodiments, and other original content. On the other hand, users can also determine the specific scope of protection of the technology through the list of claims; as well as understand the changes in the life cycle of the technology with the presentation of the patent timeline. Login to view more.

Similar technology patents

Measurement method of thickness of subsurface damaged layer of bucky optical material

InactiveCN101672625AHigh precisionStrong engineering applicability
Owner:XI AN JIAOTONG UNIV

Classification and recommendation of technical efficacy words

  • Accurate tracking
  • High precision

Target detection tracking method based on TLD algorithm

InactiveCN107392210AHigh precisionaccurate tracking
Owner:INST OF OPTICS & ELECTRONICS - CHINESE ACAD OF SCI

Depth tracking device for logging while drilling

ActiveCN101100939Aavoid slipping problemsaccurate tracking
Owner:CHINA NAT OFFSHORE OIL CORP +1

Automatically tracking device for elevation rotating shaft fixed solar receiver

InactiveCN101201628AReasonable structureaccurate tracking
Owner:ANHUI VOCATIONAL COLLEGE OF ELECTRONICS & INFORMATION TECH +1

Finite time stability control system with speed observer based on PC+FPGA

InactiveCN104155909Aaccurate tracking
Owner:GUANGDONG OCEAN UNIVERSITY

Method for forecasting short-term power in wind power station

InactiveCN102102626AHigh precision
Owner:NORTH CHINA ELECTRIC POWER UNIV (BAODING) +1

Numerical control machine tool wear monitoring method

InactiveCN102091972AHigh precisionReal-time monitoring of tool wear
Owner:HUAZHONG UNIV OF SCI & TECH +1

Advertisement recommendation method and apparatus

ActiveCN104965890AHigh precisionPrecise screening
Owner:SHENZHEN TENCENT COMP SYST CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products