CN109145874A - Measure application of the difference in the detection of obstacles of Autonomous Vehicle visual response part between video successive frame and its convolution characteristic pattern - Google Patents
Measure application of the difference in the detection of obstacles of Autonomous Vehicle visual response part between video successive frame and its convolution characteristic pattern Download PDFInfo
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Abstract
Measure application of the difference in the detection of obstacles of Autonomous Vehicle visual response part between video successive frame and its convolution characteristic pattern, the video belonged in computer vision application understands field, the type of information can be obtained in order to solve to increase convolutional neural networks, to convolutional neural networks different time sections real-time road temporal information difference understandability problem, using the numerical value of the difference mean value of maximum time information as a part of the neural network model loss function of the detection of obstacles of Autonomous Vehicle visual response part, participate in the gradient descent procedures in network backpropagation, effect is to can be improved the neural network model to understand the order of accuarcy in relevant a variety of applications in video.
Description
Technical field
The invention belongs to the videos in computer vision application to understand field, specifically a kind of measurement video successive frame
The method of difference and its application in the detection of obstacles of Autonomous Vehicle visual response part between its convolution characteristic pattern.
Background technique
While deep learning utilizes the model realization of neural network structure building end-to-end application mode, model itself
The degree of reliability that ensure that model for the storage capacity of key message in huge data, makes deep learning model compared to tradition
There is incomparable advantage for algorithm, is ground in of short duration several years by numerous scholars of image, voice, text field
Study carefully and achieves significant progress.
Target detection, target classification, target identification, Target Segmentation in computer vision technique etc. are directed to single-frame images
In, deep learning can access the correspondence model for meeting practical landing demand precision.Faster-RCNN algorithm, which is used as, works as
The basic calculation structure of lower plurality of target detection algorithm is examined using suggestion areas and the dual structure for extracting feature convolution in target
Suggestion areas is generated confidence level, convolution feature weight and the final output target detection of window by phase mutual feedback during survey
As a result accuracy is associated formula calculating, is allowed to promote fitting degree jointly during neural network forward and reverse propagation,
It is finally reached excellent effect.Depth residual error neural network all shows good in the direction that multiple computer visions are applied
Effect, it handles the information exchanged between neuron by the method for stage introducing shorting layer, makes neural network just
Become very smooth to transmittance process, so that the gradient effectively solved in deep neural network disappears and gradient explosion issues.
The Target Segmentation neural network side of algorithm as a kind of classics OSVOS (One ShotVideo Object Segmentation)
The part Calculation of the shunted current of image zooming-out prospect and profile is greater than a degree of contour area with foreground mask registration by method
As final segmentation result, have good robustness Target Segmentation.
With the increasingly maturation for single-frame images related application technology, further, to logic between successive image frame
The understanding of information, i.e., the Research Requirements understood the continuous frame time information of video are also suggested.It is acted to the pedestrian in video
Classify in this research direction, there are two types of most important technological means, respectively utilizes the binary-flow network of Optic flow information and 3D volumes
Product neural network.Binary-flow network is input to two networks using the RGB image of video frame and light stream image as input data
The training of model is carried out, the judgement information fusion calculation that will be exported each other, to obtain final pedestrian's classification of motion result.3D
Convolutional neural networks handle continuous multiple image using 3 dimension convolution kernels, remain the temporal information of video successive frame, thus
To reliably classification results.But it is accurate under practical application scene since video understands that the development time in direction is not long
Degree can not be satisfactory.More and more scholars think that existing method can not accurately extract the time letter of video successive frame
Breath, is not enough to reach application demand so as to cause the accuracy of model, needs to be further improved original method.
Summary of the invention
The type of information can be obtained in order to solve to increase convolutional neural networks, to convolutional neural networks in different time sections reality
The understandability problem of Shi Lukuang temporal information difference, the following technical solutions are proposed by the present invention: a kind of measurement video successive frame and
Application of the difference in the detection of obstacles of Autonomous Vehicle visual response part between its convolution characteristic pattern.
Further, the difference that difference between video successive frame and its convolution characteristic pattern obtains maximum time information is measured, it will
Neural network mould of the numerical value of the difference mean value of maximum time information as the detection of obstacles of Autonomous Vehicle visual response part
A part of type loss function participates in the gradient descent procedures in network backpropagation, makes the neural network of video Activity recognition
Weight gradient do not determine the descent direction of gradient according to output valve and the difference size of true value only, while also towards subtracting
The direction of small Largest Mean values of disparity is updated, and makes the weight parameter of the neural network model convolution kernel of video Activity recognition
It is updated towards the direction for reducing Largest Mean values of disparity.
Further, the difference calculation method of maximum time information is as follows:
Step 1: to raw video image frame xiAnd the corresponding convolution characteristic pattern of the picture frameObtain adjacent two
A image is as one group of temporal information element to be calculated in set；
Step 2: obtaining data with dimension the second raw video image set P'n-1With the second convolution characteristic pattern set Q
'n-1；
Step 3: the third raw video image set f (x) mapped and third convolution feature set of graphs f (xc)；
Step 4: obtaining the Largest Mean difference of temporal information.
The utility model has the advantages that
(1) reliable temporal information difference is obtained using temporal information differences method, it can be in the instruction of convolutional neural networks
Good utilization is able to during practicing.By the temporal information difference between former input video successive frame and its convolution characteristic pattern come rich
The gradient information of rich neural network makes to train the process of neural network model more reliable, and final lift scheme is to input data
The understandability of temporal information.Using temporal information difference parameter as a part of loss function, it is made to participate in convolutional Neural net
The gradient descent procedures of network backpropagation, since gradient descent procedures itself can be according to the numerical value of loss function come in network
The gradient of each convolution kernel carries out corresponding derivation and update, using final mesh of the numerical value as backpropagation for reducing loss function
's.And while also regarding temporal information difference parameter as loss function a part, so that convolutional neural networks is being updated each volume
Not only used the difference size information of output valve and true value when the gradient of product core, also used Largest Mean difference as
The calculation basis of gradient updating causes the gradient parameter of each convolution kernel to carry out more towards the direction for reducing Largest Mean difference
Newly.As gradient declines, the similarity of two groups of temporal informations is intended to increase, to guarantee that convolutional neural networks can be better
Retain the temporal information of initial data.
(2) in temporal information differences method with renewable core Hilbert space possess the complete inner product space, will
Information MAP can be intact to the space reservation initial data property, guarantee temporal information differences method calculate data foot
It is enough reliable, it can effectively embody the temporal information difference between video successive frame and its convolution characteristic pattern.Meanwhile the mapping space
Itself has steady regularity, it is ensured that method has enough continuitys, i.e., with the increase of input data set, side
Method can also rapidly converge to its desired value.
(3) the feature calculation emphasis of existing common convolutional neural networks is only in that on scene information, can not be transported well
Use temporal information, this method convolutional neural networks binding time information gap method is made it has acquired video successive frame with
Temporal information difference between its convolution characteristic pattern, the type of information can be obtained by improving network, to increase convolutional neural networks
For the understandability of video data.By temporal information difference between measurement video successive frame and its convolution characteristic pattern and make its ginseng
With back-propagation process, to also can be improved the mind while lift scheme continuous to video frame time information understandability
The order of accuarcy in relevant a variety of applications is understood in video through network model, such as promotes the correctness of video actions classification,
The accuracy of video Activity recognition is improved, guarantees effective output etc. of unusual checking in monitor video.Further exist
Also it is capable of providing certain miscellaneous function under other application scenarios, such as is provided reliably in the Classical correlation application for video of taking photo by plane
Temporal information difference, understanding to non-static object is promoted in the obstacle detection system of Autonomous Vehicle visual response part
Ability increases the understandability to different time sections real-time road temporal information difference, gives Autonomous Vehicle subsequent operation anticipation, road
The operations such as diameter planning, which provide, effectively to help.
(4) since the calculating logic of this method carries out metric calculation mainly for the otherness between different data, lead to
It crosses to different input datas using suitable cross-cutting conversion means, this method can be made to be used not only for measurement video and connected
Temporal information difference between continuous frame and its convolution characteristic pattern, can also assist in the related application for measuring continuous voice messaging
Task: such as the voice data of different places dialect even different language is extracted and is compared, is obtained between different language
Pitch disparity and syntactic structure difference, make neural network have according to voice data the ability for judging different language type；Or
Person is directed to the related application task of continuous text information: calculating is compared by the text information difference to different types,
Neural network is set to have according to text information the ability for judging text type of genre；Other data class can be used Deng other
The related application of type different information possesses good cross-cutting generalization.
Detailed description of the invention
Fig. 1 is the schematic diagram that this method handles a certain group of video successive frame and its convolution characteristic pattern
Fig. 2 is two original video sequential frame images in embodiment 1
Fig. 3 is the corresponding convolution characteristic pattern of two width original video sequential frame images in embodiment 1
Fig. 4 is the resulting quantization time information gap distance of embodiment 1
Fig. 5 is two original video sequential frame images in embodiment 2
Fig. 6 is the corresponding convolution characteristic pattern of two width original video sequential frame images in embodiment 2
Fig. 7 is the resulting quantization time information gap distance of embodiment 2
Fig. 8 is two original video sequential frame images in embodiment 3
Fig. 9 is the corresponding convolution characteristic pattern of two width original video sequential frame images in embodiment 3
Figure 10 is the resulting quantization time information gap distance of embodiment 3
Figure 11 is two original video sequential frame images in embodiment 4
Figure 12 is the corresponding convolution characteristic pattern of two width original video sequential frame images in embodiment 4
Figure 13 is the resulting quantization time information gap distance of embodiment 4
Figure 14 is two original video sequential frame images in embodiment 5
Figure 15 is the corresponding convolution characteristic pattern of two width original video sequential frame images in embodiment 5
Figure 16 is the resulting quantization time information gap distance of embodiment 5
Specific embodiment
Present invention is further described in detail with specific embodiment with reference to the accompanying drawing:
Embodiment: the present embodiment is in order to deepen neural network for the understandability of temporal information in video successive frame, needle
A kind of method is devised to convolutional neural networks to calculate the temporal information difference between video successive frame and its convolution characteristic pattern, it should
Method can be a kind of network model that can further be promoted to the metric difference side of time comprehension of information ability by software realization
Method.It is using the quantization time information gap calculated between the video successive frame and its convolution characteristic pattern that measurement obtains, difference is anti-
It is fed in the training process of neural network, neural network is enable to apply to temporal information difference, raising pair when updating weight
The understandability of the continuous interframe temporal information of video.
The present embodiment method can between robust calculation different field sample difference distance intension, be innovatively incorporated into
Convolutional neural networks calculate to use to the temporal information difference between video successive frame and its convolution characteristic pattern, enable the network to
Deepen the understanding to temporal information in video successive frame.
Wherein, video successive frame representative converts original video in the image data as unit of frame, continuous front and back
Two frames or arbitrary frame image.The representative of convolution characteristic pattern obtains raw image data after convolution algorithm, compared to original image
For with certain specific aim characteristic properties image data.
Wherein, temporal information represents a kind of need by carrying out difference operation to the continuous frame data of video, come what is obtained
The acquisition modes of time difference data between same video lower different moments picture frame, corresponding convolution characteristic pattern temporal information are same
Reason.Since video successive frame and its convolution characteristic pattern have source relationship, so the temporal information of two groups of data can also be regarded
To possess direct connection, the connection of reasonable utilization between the two understands that field and its related application have certain valence for video
Value.
The present embodiment is achieved through the following technical solutions, poor between a kind of measurement video successive frame and its convolution characteristic pattern
Different method, above system and device are to be obtained by this method, which specifically comprises the following steps:
Step 1: being image by Video Quality Metric, the video frame images that sum is n are obtained, all raw video images are taken out
Frame xiAnd the corresponding convolution characteristic pattern of the picture frameWherein i represents frame number.Original image and convolution characteristic pattern are divided into
Two set, respectively in set, two adjacent images are as one group of temporal information element to be calculated in set.I.e. to original graph
Image set closes Pn-1With convolution feature set of graphs Qn-1Interior data carry out certain division processing, make each group in set it is to be calculated when
Between information element be to be made of two adjacent image datas of the image collection, such as x1With x2It is one group, x2With x3It is one group, volume
Product feature set of graphs is similarly.Wherein original image set may be expressed as:
Pn-1={ [x1,x2],[x2,x3],[x3,x4]…[xn-1,xn]}
Convolution feature set of graphs may be expressed as:
Step 2: carrying out zero padding to the dimension of all different size data rises dimension, or the processing for the dimensionality reduction that zero-suppresses, dimension is obtained
Treated original image set P'n-1With dimension treated convolution feature set of graphs Q'n-1, two are gathered interior all data
Dimension size is all identical, this operation can be convenient for the progress of metric calculation；
Step 3: all data after two dimensions are handled in set carry out space reflection calculating and are averaging, obtain
Original image set f (x) and convolution feature set of graphs f (x after mapping after mappingc)；
Wherein f represents the continuous function collection on the renewable core Hilbert space being mapped, and data are reflected in f (x) representative
Function result after penetrating；
Wherein renewable core Hilbert space is a kind of to be constituted using renewable kernel function as basic data in space
The inner product space with completeness.The Limit Operation that completeness represents any function in the space cannot all be detached from the space
Range, the inner product space are to be conjugated symmetrical, line between arbitrary data can all carry out inner product and meet data in a kind of any dimensional space
Property and orthotropicity space, any space for meeting above-mentioned two condition all be referred to as Hilbert space.Renewable core letter
Number represents the kernel function for meeting and possessing Eigenvalue and eigenfunction and arbitrary characteristics function all pairwise orthogonals in infinite dimensional space；
Step 4: by number after the liter dimension of two data of every group of temporal information element to be calculated in two set, mapping
According to difference operation is carried out, the difference of the mapping data of each group time dimension element to be calculated in each set is calculated separately, to difference
Value sums and calculates average value, calculates original image set P'n-1Mapping set the average value and convolution feature atlas
Close Q'n-1Mapping set the mean value, and to described two mean values make difference and square, the Largest Mean for obtaining temporal information is poor
It is different.It may be expressed as: with formula
Obtain the quantized result of temporal information difference between original image and convolution characteristic pattern.
Step 5: being participated in using the numerical value of Largest Mean difference as a part of convolutional neural networks model loss function
Gradient descent procedures in network backpropagation keep difference of the weight gradient of network not only according to output valve and true value big
The small descent direction to determine gradient, while being also updated towards the direction for reducing Largest Mean values of disparity, make convolution kernel
Weight parameter towards reduce Largest Mean values of disparity direction update.
Above-mentioned technical proposal is a kind of method of difference between measurement video successive frame and its convolution characteristic pattern, and succinct says,
It includes the following steps:
Step 1: being image by Video Quality Metric, the video frame images that sum is n are obtained, all raw video images are taken out
Frame xiAnd the corresponding convolution characteristic pattern of the picture frameWherein i represents frame number, by raw video image and convolution characteristic pattern
It is divided into two set, in each set, two adjacent images are as one group of temporal information element to be calculated in set:
First raw video image set expression are as follows:
Pn-1={ [x1,x2],[x2,x3],[x3,x4]…[xn-1,xn]}
First convolution characteristic pattern set expression are as follows:
Step 2: the carry out zero padding of the data different to dimension rises dimension or the dimensionality reduction that zero-suppresses so that the same dimension of each data, obtains
To the second raw video image set P'n-1With the second convolution characteristic pattern set Q'n-1。
Step 3: by the second raw video image set P'n-1With the second convolution characteristic pattern set Q'n-1Set in institute
There are data to carry out space reflection calculating and be averaging, obtains third raw video image set f (x) and third convolution feature atlas
Close f (xc)；Wherein f represents the continuous function collection on the renewable core Hilbert space being mapped.
Step 4: to third raw video image set f (x), third convolution feature set of graphs f (xc), it calculates separately each
The difference of the mapping data of each group same dimension temporal information element to be calculated in set sums to difference and calculates average value,
To described two mean values make difference and square, obtain the Largest Mean difference of temporal information, the maximum of temporal information indicated with formula
Mean value difference are as follows:
By above-mentioned, the present embodiment proposes a kind of method to measure the side of difference between video successive frame and its convolution characteristic pattern
Method is calculated, finally by dimension processing, space reflection and the difference to the continuous frame data of video and its convolution feature diagram data
Obtain the quantized values of temporal information difference between two groups of data.The temporal information difference obtained by this method can be fed back and be arrived
The training process of convolutional neural networks, to promote the understanding journey of neural network interframe temporal information difference continuous for video
Degree, influences the subsequent other application that video understands direction.
It is as follows that scheme relevant to the disclosure is disclosed in the prior art:
2016, application for a patent for invention " video understanding method and device " (publication number: CN107563257A) disclosed one
Kind is estimated based on scene depth and is obtained depth scene information, thus the side for further being understood scene content and being analyzed
Method, the invention mainly obtain the depth information of scene with a variety of different neural network structures.The difference lies in that this implementation
Example is mainly directed to the temporal information difference between the continuous frame data of original video and its convolution feature diagram data using calculation method,
Rather than the depth information in video scene is obtained by Multi-network.
2017, application for a patent for invention " a kind of image difference detection method based on steadiness factor method " was (open
Number: CNIO7705295A), disclose it is a kind of under Same Scene, different time, different perspectives data information obtained into
Row models and analyzes processing, to obtain steady scene information.The difference lies in that the present embodiment is mainly for video successive frame
Carry out the temporal information difference between measurement with its convolution characteristic pattern, reduces video successive frame by training convolutional neural networks
The method of temporal information difference size reinforces convolutional neural networks to the grasp energy of temporal information between its convolution characteristic pattern
Power increases network model for the understandability of video time information, and the data of Same Scene are believed under non-used different condition
The analysis processing of breath is to obtain different information.
2017, application for a patent for invention " a kind of video understanding method based on deep learning " (publication number:
CNIO7909014A), one kind is disclosed by three kinds of LSTM network, C3D algorithm and PCA algorithm method associative operations, further
Obtain the video understanding method of the stronger video sentence information to be detected of reliability.The difference lies in that the present embodiment utilizes the time
Information gap method measures the temporal information difference between video successive frame and its convolution characteristic pattern, finally obtains the difference of quantization
Heteromerism value with the sentence comprehension of video as a result, be not associated with.
Embodiment 1:
This embodiment is for one group of original video sequential frame image as shown in Figure 2 and correspondence convolution as shown in Figure 3
The distance metric that characteristic pattern carries out calculates, and Fig. 4 is calculated results.
Embodiment 2:
This embodiment is for one group of original video sequential frame image as shown in Figure 5 and corresponding convolution as shown in FIG. 6
The distance metric that characteristic pattern carries out calculates, and Fig. 7 is calculated results.
Embodiment 3:
This embodiment is for one group of original video sequential frame image as shown in Figure 8 and correspondence convolution as shown in Figure 9
The distance metric that characteristic pattern carries out calculates, and Figure 10 is calculated results.
Embodiment 4:
This embodiment is for one group of original video sequential frame image as shown in figure 11 and corresponding volume as shown in figure 12
The distance metric that product characteristic pattern carries out calculates, and Figure 13 is calculated results.
Embodiment 5:
This embodiment is for one group of original video sequential frame image as shown in figure 14 and corresponding volume as shown in figure 15
The distance metric that product characteristic pattern carries out calculates, and Figure 16 is calculated results.
The preferable specific embodiment of the above, only the invention, but the protection scope of the invention is not
It is confined to this, anyone skilled in the art is in the technical scope that the invention discloses, according to the present invention
The technical solution of creation and its inventive concept are subject to equivalent substitution or change, should all cover the invention protection scope it
It is interior.
Claims (3)
1. obstacle quality testing of the difference in Autonomous Vehicle visual response part between a kind of measurement video successive frame and its convolution characteristic pattern
Application in survey.
2. application as described in claim 1, which is characterized in that difference obtains between measurement video successive frame and its convolution characteristic pattern
The difference of maximum time information, using the numerical value of the difference mean value of maximum time information as the barrier of Autonomous Vehicle visual response part
Hinder a part of the neural network model loss function of analyte detection, participates in the gradient descent procedures in network backpropagation, make to regard
The weight gradient of the neural network of frequency Activity recognition does not only determine gradient according to output valve and the difference size of true value
Descent direction, while being also updated towards the direction for reducing Largest Mean values of disparity, make the nerve net of video Activity recognition
The weight parameter of network model convolution kernel is updated towards the direction for reducing Largest Mean values of disparity.
3. application as claimed in claim 2, which is characterized in that the difference calculation method of maximum time information is as follows:
Step 1: to raw video image frame xiAnd the corresponding convolution characteristic pattern of the picture frameObtain two adjacent figures
As one group of temporal information element to be calculated in set；
Step 2: obtaining data with dimension the second raw video image set P 'n-1With the second convolution characteristic pattern set Q 'n-1；
Step 3: the third raw video image set f (x) mapped and third convolution feature set of graphs f (xc)；
Step 4: obtaining the Largest Mean difference of temporal information.
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| 顾婷婷: "基于帧间信息提取的单幅红外图像深度估计", 《激光与光电子学进展》 * |
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| CN109145874B (en) | 2023-07-04 |
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