CN107396094B - Automatic testing method towards camera single in multi-cam monitoring system damage - Google Patents
Automatic testing method towards camera single in multi-cam monitoring system damage Download PDFInfo
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Abstract
The present invention relates to a kind of automatic testing methods towards camera single in multi-cam monitoring system damage, the specific steps are as follows: (1) acquires, make training test sample collection；(2) constructing neural network；(3) training neural network parameter observes network error, until network convergence；(4) with trained network application deployment, and real-time monitoring.The method that the present invention uses is based on convolutional neural networks, and convolutional neural networks are developed recentlies, cause a kind of efficient identification method paid attention to extensively.Raw video image passes through convolutional layer, pond layer, the processing of the series of features such as active coating, the classification of final output image.The method of the present invention can substitute manual operation completely, have detection it is automatic, in real time, efficiently, precision it is accurate, do not need additional hardware, it is at low cost the features such as.
Description
Technical field
The present invention relates to a kind of automatic testing methods towards camera single in multi-cam monitoring system damage, belong to
Field of video monitoring.
Background technique
Video monitoring system is the physical basis monitored in real time to every profession and trade key sector or important place, management department
Door can obtain valid data image information by it, and the process of paroxysmal abnormality event is timely monitored and remembered, is used
It commands efficiently, in time with offer and quickly arranges police strength, settles a case.The video monitoring system of multi-cam composition, it is real
The dual function for having showed monitoring with communication, meets the monitoring of the every field such as traffic, water conservancy, oil field, bank, telecommunications comprehensively and answers
Anxious commander's demand.Reaching the demand just necessarily requires each camera in monitoring system component part must normal work
Make.Since monitoring system is usually round-the-clock, all the period of time, twenty four hours is monitored incessantly, so just necessarily requiring
System energy when some camera has damage to occur is automatic, real time notification system maintenance person repairs replacement.
In existing video monitoring system, the maintenance and monitoring of camera are relied primarily on manually, can not achieve real-time prison
Control, and cannot determination promptly and accurately which camera damage, need gradually to check, time-consuming and laborious, inefficiency.
Summary of the invention
In view of the defects existing in the prior art, the object of the present invention is to provide one kind towards single in multi-cam monitoring system
The automatic testing method of a camera damage, have detection it is automatic, in real time, efficiently, precision it is accurate, do not need additional hardware, at
The features such as this is low.
The technical solution adopted by the present invention to solve the technical problems is:
A kind of automatic testing method towards camera single in multi-cam monitoring system damage, the specific steps are as follows:
(1) it acquires, makes training test sample collection；
(1a) acquires original image: while each n of image in m monitor camera are acquired, wherein m, n are positive integer,
300 × 300 sizes are zoomed to, it is corresponding to be placed in m file, wherein n picture is according to acquisition in each file
Chronological order is numbered from 1 to n；
(1b) makes damaged image: to number n since number 1, whether damaging this with 50% probability selection every time
Number picture；If selection is, a file is randomly choosed from m file, the selected probability of each file is
1/m；Increasing randomly shaped, the solid-color image block of random color, the image block on the number picture in the file chosen
Area accounts for 30% or more of the picture area, represents video camera damage；Every image all generates text file of the same name, in file
Holding is picture label, if picture damage label is 1, not damaging label is 0；
(1c) training stage sequentially inputs m images according to m file, in articulamentum, m 300 × 300 × 3
300 × 300 × (3 × m) sizes are merged on channel, and it is 1 that label, which is set as the number image label in file,
Folder tabs.Such as: if certain number m images, the image label of file 1 is 1, the number in remaining m-1 file
The label of image is 0, then the data label after merging is set as 1, represents first kind sample；If should in m file
Number image label is 0, then label is 0 after merging, and represents null class sample.
(2) constructing neural network；
The input of network is by the realtime graphic frame of m video camera of permanent order input, wherein every image is scaled
To 300 × 300 × 3 sizes, i.e. 300 pixels are wide, 300 pixel height, 3 channels of every cromogram；Next articulamentum
M camera image is merged into 300 × 300 × (3 × m) sizes on a passage；M+1 value finally is exported in full articulamentum, point
Not Biao Shi m+1 class probability size, if the maximum probability of null class, then it represents that m camera all works normally；If the first kind
Maximum probability, indicate camera 1 work it is abnormal；If the maximum probability of the second class, it is abnormal to indicate that camera 2 works；If
It is abnormal to indicate that camera 3 works for the maximum probability of third class；If the maximum probability of m class, indicate that camera m work is abnormal.
(3) training neural network parameter observes network error, until network convergence；
Sample is inputted network and merges image in articulamentum by (3a), after generating sample label, is grasped using neural network
Make, exports m+1 value according to following formula in full articulamentum, represent the probability of m+1 class；
Zi=Wi*X+b
Wherein, X is one layer on full connection output layer of network output matrix, WiIt is i-th of output unit of full articulamentum
Weight matrix, b are the preamble of full articulamentum, ZiRefer to the output valve of i-th of output unit, i has m+1 value altogether from 0~m；Point
The class probability of m+1 class is not represented；
All kinds of probability output values of full articulamentum are input to SoftmaxWithLoss layers by (3b), according to following formula meter
The penalty values of network, and the parameter of backpropagation training network are calculated,
Wherein, k indicates classification, and y indicates the sample label, ZiRefer to the probability value of i-th of sample.
(4) with trained network application deployment, and real-time monitoring；
The above trained network is disposed, the input of network is the realtime graphic by m video camera of permanent order input
Frame, wherein every image is scaled to 300 × 300 × 3 sizes, i.e. 300 pixels are wide, 300 pixel height, every cromogram 3
A channel；M camera image is merged into 300 × 300 × (3 × m) sizes by next articulamentum on a passage；It is rolled up again
Lamination, pond layer, the operation of active coating；M+1 value finally is exported in full articulamentum, respectively indicates the size of m+1 class probability, such as
The maximum probability of fruit null class, then it represents that m camera all works normally；If the maximum probability of the first kind, indicate that camera 1 works
It is abnormal；If the maximum probability of the second class, it is abnormal to indicate that camera 2 works；If the maximum probability of third class, camera is indicated
3 work are abnormal；If the maximum probability of m class, indicate that camera m work is abnormal；With this, multi-cam monitoring system is realized
In single camera damage automatic detection.
Compared with prior art, the invention has the following advantages that
The method that the present invention uses is based on convolutional neural networks, and convolutional neural networks are developed recentlies, causes wide
A kind of efficient identification method of general attention.Raw video image passes through convolutional layer, pond layer, at the series of features such as active coating
Reason, the classification of final output image.The method of the present invention can substitute manual operation completely, automatic with detection, real-time, efficient,
The features such as precision is accurate, does not need additional hardware, at low cost.
Detailed description of the invention
Fig. 1 is the work flow diagram of embodiment of the present invention method.
The schematic diagram of Fig. 2 the method for the present invention training sample and its class label.
Fig. 3 is the neural network structure figure of the method for the present invention phrase of operation and implementation.
Specific embodiment
With reference to the accompanying drawing, specific embodiments of the present invention are described further.
Referring to Fig.1, the embodiment of the present invention is described in further detail with m=4, n=20000:
A kind of automatic testing method towards camera single in multi-cam monitoring system damage, the specific steps are as follows:
Step 1, it acquires, makes training test sample collection:
Step 1 acquires original image: while image each 20000 in four monitor cameras are acquired, zoom to 300
× 300 sizes, it is corresponding to be placed in four files, wherein 20000 pictures are according to acquisition time elder generation in each file
Sequence afterwards is numbered from 00001 to 20000.
Step 2 makes damaged image: to number 20000 since number 00001, being with 50% probability selection every time
It is no to damage the number picture；If selection is, a file is randomly choosed from four files, each file is selected
In probability be 25%；Increasing randomly shaped, the solid-color image of random color on the number picture in the file chosen
Block, the image block area account for 30% or more of the picture area.Represent video camera damage.Every image all generates text of the same name
File, file content are picture label, if picture damage label is 1, not damaging label is 0.
Step 3, training stage sequentially input four images according to four files, in articulamentum, four 300 × 300
× 3 are merged into 300 × 300 × 12 sizes on a passage, and it is 1 that label, which is set as the number image label in file,
Folder tabs.Such as: if four images of certain number, the image label of file 1 is 1, the number in excess-three file
The label of image is 0, then the data label after merging is set as 1, represents first kind sample；If should in four files
Number image label is 0, then label is 0 after merging, and represents null class sample；Fig. 2 summarizes this method classification and label shows
Meaning: classification 0 indicates that four cameras all work normally；Classification 1 indicates first camera damage in four cameras；Classification 2
Indicate second camera damage in four cameras；Classification 3 indicates third camera damage in four cameras；Classification 4
Indicate the 4th camera damage in four cameras.
Step 2, neural network used in this method is constructed:
Fig. 3 is this method schematic network structure, it is characterized in that: the input of network is four by permanent order input
The realtime graphic frame of video camera, wherein every image is scaled to 300 × 300 × 3 sizes, i.e. 300 pixels are wide, 300 pictures
It is plain high, 3 channels of every cromogram；Four camera images are merged into 300 × 300 × 12 by next articulamentum on a passage
Size；5 values finally are exported in full articulamentum, respectively indicate the size of five class probability, if the maximum probability of null class, table
Show that four cameras all work normally；If the maximum probability of the first kind, it is abnormal to indicate that camera 1 works；If the second class is general
Rate is maximum, and it is abnormal to indicate that camera 2 works；If the maximum probability of third class, it is abnormal to indicate that camera 3 works；If the 4th
It is abnormal to indicate that camera 4 works for the maximum probability of class.
Step 3, with sample training network:
Sample is inputted network and merges image in articulamentum, after generating sample label, using neural network by step 1
Operation, exports 5 values, represents the probability of five classes in full articulamentum according to following formula；
Zi=Wi*X+b
Wherein, X is one layer on full connection output layer of network output matrix, WiIt is i-th of output unit of full articulamentum
Weight matrix, b are the preamble of full articulamentum, ZiRefer to the output valve of i-th of output unit.I has 5 values altogether from 0~4.Respectively
Represent the class probability of five classes.
All kinds of probability output values of full articulamentum are input to SoftmaxWithLoss layers, according to following formula by step 2
The penalty values of network, and the parameter of backpropagation training network are calculated, network error declines always during practicing, and measuring accuracy is not
Disconnected to improve, when network convergence, training is completed, and obtains one group of high network parameter of detection accuracy.
Wherein, k indicates classification, and y indicates the sample label, ZiRefer to the probability value of i-th of sample.
Step 4, with trained network application deployment:
The above trained network is disposed, the input of network is the realtime graphic by four video cameras of permanent order input
Frame, wherein every image is scaled to 300 × 300 × 3 sizes, i.e. 300 pixels are wide, 300 pixel height, every cromogram 3
A channel；Four camera images are merged into 300 × 300 × 12 sizes by next articulamentum on a passage；Convolution is carried out again
Layer, pond layer, the operation such as active coating；5 values finally are exported in full articulamentum, respectively indicate the size of five class probability, if the
The maximum probability of null class, then it represents that four cameras all work normally；If the maximum probability of the first kind, the work of camera 1 is indicated not
Normally；If the maximum probability of the second class, it is abnormal to indicate that camera 2 works；If the maximum probability of third class, camera 3 is indicated
It works abnormal；If the maximum probability of the 4th class, it is abnormal to indicate that camera 4 works.With this, multi-cam monitoring system is realized
In single camera damage automatic detection.
Claims (1)
1. a kind of automatic testing method towards camera single in multi-cam monitoring system damage, which is characterized in that specific
Steps are as follows:
(1) it acquires, makes training test sample collection, its step are as follows:
(1a) acquires original image: while each n of image in m monitor camera are acquired, wherein m, n are positive integer, scaling
It is corresponding to be placed in m file to 300 × 300 sizes, wherein n picture is according to acquisition time elder generation in each file
Sequence afterwards is numbered from 1 to n；
(1b) makes damaged image: to number n since number 1, whether damaging the number with 50% probability selection every time
Picture；If selection is, a file is randomly choosed from m file, the selected probability of each file is 1/m；
Increasing randomly shaped, the solid-color image block of random color, the image block area on the number picture in the file chosen
30% or more of the picture area is accounted for, then represents video camera damage；Every image all generates text file of the same name, file content
For picture label, if picture damage label is 1, not damaging label is 0；
(1c) training stage sequentially inputs m images according to m file, in articulamentum, m 300 × 300 × 3 logical
It is merged into 300 × 300 × (3 × m) sizes on road, and label is set as the text that the number image label is 1 in file
Part presss from both sides label；
(2) neural network used in this method is constructed, its step are as follows:
The input of (2a) network is by the realtime graphic frame of m video camera of permanent order input, wherein every image is scaled
To 300 × 300 × 3 sizes, i.e. 300 pixels are wide, 300 pixel height, 3 channels of every cromogram；Next articulamentum
M camera image is merged into 300 × 300 × (3 × m) sizes on a passage；M+1 value finally is exported in full articulamentum, point
Not Biao Shi m+1 class probability size, if the maximum probability of null class, then it represents that m camera all works normally；If the first kind
Maximum probability, indicate camera 1 work it is abnormal；If the maximum probability of the second class, it is abnormal to indicate that camera 2 works；If
It is abnormal to indicate that camera 3 works for the maximum probability of third class；If the maximum probability of m class, indicate that camera m work is abnormal；
(3) sample training network is used, its step are as follows:
Sample is inputted network and merges image in articulamentum by (3a), after generating sample label, is operated using neural network,
M+1 value is exported, the probability of m+1 class is represented according to following formula in full articulamentum；
Wherein, X is one layer on full connection output layer of network output matrix,It is the weight of i-th of output unit of full articulamentum
Matrix, b are the preambles of full articulamentum,Refer to the output valve of i-th of output unit, i has m+1 value altogether from 0 ~ m；Point
The class probability of m+1 class is not represented；
All kinds of probability output values of full articulamentum are input to SoftmaxWithLoss layers by (3b), calculate net according to following formula
The penalty values of network, and the parameter of backpropagation training network,
Wherein, k indicates classification, and y indicates sample label,Refer to the probability value of i-th of sample；
(4) with trained network application deployment, its step are as follows:
(4a) disposes the above trained network, and the input of network is the realtime graphic by m video camera of permanent order input
Frame, wherein every image is scaled to 300 × 300 × 3 sizes, i.e. 300 pixels are wide, 300 pixel height, every cromogram 3
A channel；M camera image is merged into 300 × 300 × (3 × m) sizes by next articulamentum on a passage；It is rolled up again
Lamination, pond layer activate layer operation；M+1 value finally is exported in full articulamentum, respectively indicates the size of m+1 class probability, if
The maximum probability of null class, then it represents that m camera all works normally；If the maximum probability of the first kind, the work of camera 1 is indicated not
Normally；If the maximum probability of the second class, it is abnormal to indicate that camera 2 works；If the maximum probability of third class, camera 3 is indicated
It works abnormal；If the maximum probability of m class, indicate that camera m work is abnormal；With this, multi-cam monitoring system is realized
In single camera damage automatic detection.
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