CN110411605A - A kind of infrared survey human body temperature modification method - Google Patents
A kind of infrared survey human body temperature modification method Download PDFInfo
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- CN110411605A CN110411605A CN201910712934.0A CN201910712934A CN110411605A CN 110411605 A CN110411605 A CN 110411605A CN 201910712934 A CN201910712934 A CN 201910712934A CN 110411605 A CN110411605 A CN 110411605A
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Abstract
A kind of infrared survey human body temperature modification method, limited area including automatic learning foundation crowd temperature acquisition, based on acquisition passenger's face temperature data in limited area, passenger's face temperature data is analyzed, judge whether to need darg time being divided into different time sections, such as judgement needs for be divided into the darg time different time sections, calculates basic crowd's temperature in different time periods；Determine basis crowd's temperature highest period, face temperature data in other times section is adjusted, keep other times section equal with basic crowd's temperature of temperature highest period, after adjusting face temperature data, all face temperature datas are ranked up, the scope of subject and temperature alarming value of Temperature Distribution are calculated；The temperature of acquisition is modified according to obtained scope of subject and temperature alarming value.The present invention adapts to different geographical weather, Changes in weather, human body one day Temperature changing in the morning, afternoon and evening；The dependence to staff's experience is reduced into degree.
Description
Technical field
The present invention relates to infrared measurement of temperature fields, and in particular to a kind of infrared survey human body temperature modification method.
Background technique
The prior art does not consider the environmental factors such as temperature caused by regional climate, seasonal variations, humidity to body surface temperature
The influence of degree is used uniformly the identical temperature value of identical body surface temperature-compensating, carries out temperature adjustmemt.Need operator according to reality
Border situation adjusts the size of compensating parameter, higher to the skill requirement of staff.
Summary of the invention
In order to solve the above technical problems, the present invention provides a kind of infrared survey human body temperature modification method, difference is adapted to
Regional climate, Changes in weather, human body one day Temperature changing in the morning, afternoon and evening；The dependence to staff's experience is reduced into degree.The present invention
Technical solution it is as follows:
A kind of infrared survey human body temperature modification method, which comprises
Step 1, passenger's face temperature data is acquired, passenger's face temperature data of acquisition is analyzed, is judged whether
It needs darg time being divided into different time sections, if judgement needs darg time being divided into different time sections,
Calculate basic crowd's temperature in different time periods；
Step 2, determine basic crowd's temperature highest period, to the face temperature data in other times section into
Row adjustment, keeps other times section equal with basic crowd's temperature of temperature highest period, after adjusting face temperature data, to institute
There is face temperature data to be ranked up, calculates the scope of subject and temperature alarming value of Temperature Distribution；
Step 3, the temperature of acquisition is modified according to obtained scope of subject and temperature alarming value.
Further, the method also includes, before step 1, the first restriction of automatic learning foundation crowd's temperature acquisition
Region, in step 1, based on acquisition passenger's face temperature in limited area.
Further, the limited area of the automatic learning foundation crowd temperature acquisition specifically includes:
Continuous acquisition infrared image extracts face temperature data and corresponding infrared image coordinate bit from infrared image
It sets；
It counts nDay days face temperature datas and all face temperature was calculated according to nDay days of statistics face temperature datas
The mean value Avgtemp of degree, retening temperature near samming Avgtemp m face temperature data as temperature samples data,
In, m=fR1* total face temperature data quantity；
Infrared image region is divided equally into n rectangular area, counts temperature samples data in each rectangular area
Quantity nLocalNum, judges whether the quantity of all temperature samples data in rectangular area accounts for the total face temperature in the region
The fR2 or more of data bulk, if it is, the rectangular area to be retained as to the stability region of temperature acquisition；
Above-mentioned institute stability region with a grain of salt is merged, binaryzation template is saved as, is used as basic crowd's temperature acquisition
Limited area.
Further, the infrared image of acquisition is using black matrix as benchmark, and it is following that acquisition full figure is no more than black body radiation face
The infrared image of boundary's height.
Further, step 1 specifically includes:
The temperature data of primary all faces was acquired at interval of nSec seconds；
Each hour as a cycle, calculates all face temperature means of mean acquired in each small period
NTempMeanPerHour saves this day calculated all average value nTempMeanPerHour as 1 group of temperature mean value number
According to；
When daily system boot operation, from current date nToday to being pushed forward nNearDays days, the nNearDays is extracted
It temperature mean data, i.e. extraction n group temperature mean data；
Merging n group temperature mean data will be identical small in n group temperature mean data as new one group of temperature mean data
When the period the mean value of n nTempMeanPerHour the temperature of hour period is corresponded to as new one group of temperature mean data
Mean value；
Find maximum temperature mean value fTempMeanHigh and minimum equal temperature mean value in new one group of temperature mean data
FTempMeanLow, calculating temperature difference fTempMeanDiff=fTempMeanHigh-fTempMeanLow；
If temperature difference fTempMeanDiff > temperature difference preset value fTempThresh, with (fTempMeanHigh+
FTempMeanLow)/2 as the fragmentation threshold fTimeSegThresh of temperature-time section, the darg time is divided into 3
The continuous time period of period, i.e. temperature mean value lower than fTimeSegThresh is as first time period, by temperature mean value
Temperature mean value will be lower than fTimeSegThresh as second time period by the continuous time period higher than fTimeSegThresh
Another continuous time period as the third period；
The mean value of all face temperature datas of closest nNearDays days same time periods is calculated, when as dividing
Between each period after section basic crowd's temperature.
Further, if temperature difference fTempMeanDiff≤temperature difference preset value fTempThresh, did not needed one day
Working time is divided into different time sections, does not need to carry out period amendment.
Further, step 2 specifically includes:
According to basic crowd's temperature of each period obtained, basis crowd's temperature highest period is determined,
As the highest temperature period, other times section and basic crowd's temperature gap Dvalue between the highest temperature period are calculated, it will
All face temperature datas in other times section add corresponding basic crowd temperature gap Dvalue, so that other times
Basic crowd's temperature of section is equal with basic crowd's temperature of highest temperature period；
All face temperature datas in other times section to the highest temperature period and after drawing high are ranked up, and are obtained
The scope of subject [fMinTemp, fMaxTemp] and temperature alarming value fAlarmTempIr of Temperature Distribution；Wherein
FMinTemp is that maximum fR3 face temperature data and the smallest fR3 face are removed from the face temperature data of sequence
Minimum value after temperature data in remaining face temperature data, fMaxTemp are to remove most from the face temperature data of sequence
Minimum value after big fR3 face temperature data and the smallest fR3 face temperature data in remaining face temperature data,
FAlarmTempIr is the remaining face temperature after removing maximum fR4 face temperature data in the face temperature data of sequence
Maximum value of the degree in.
Further, the step 3 specifically includes:
Scope of subject and temperature alarming value based on acquisition, according to following formula to passenger's face temperature
FIrFaceTemp is mapped, and is obtained and is mapped compensated temperature fMappingTemp, and temperature adjustmemt is completed；
Wherein the calculating of temperature range compressed coefficient fCoef is as follows:
FCoef1=min (1, [(36.5-36.0)/(fMaxTempMean-fMinTempMean)])；
FCoef2=min (1, [(37.3-36.5)/(fAlarmTempIr-fMaxTempMean)])；
Wherein fMinTemp is the minimum value in the scope of subject of Temperature Distribution, and fMaxTemp is the main body model of Temperature Distribution
Maximum value in enclosing, fAlarmTempIr are temperature alarming value.
The invention has the following advantages:
The present invention proposes a kind of infrared survey human body temperature modification method, first the automatic study body temperature sample based on black body locus
This pickup area, excludes the interference in other regions, and based on the temperature samples data statistic analysis acquired in limited area, according to
The variation of temperature is segmented the darg time, calculates basic crowd's temperature of each period, and obtains shell temperature and reflect
Penetrate the temperature correction of relationship.
Detailed description of the invention
Fig. 1 is a kind of infrared survey human body temperature modification method flow chart provided in an embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiment is only present invention a part, instead of all the embodiments.Based on the present invention
In embodiment, all other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, shall fall within the protection scope of the present invention.
As shown in Figure 1, being a kind of infrared survey human body temperature modification method provided in an embodiment of the present invention, the method packet
It includes:
Step 1, passenger's face temperature data in limited area is acquired, passenger's face temperature data of acquisition is divided
Analysis, judges whether to need darg time being divided into different time sections, as judgement needs for be divided into the darg time
Different time sections calculate basic crowd's temperature in different time periods；
Step 2, determine basic crowd's temperature highest period, to the face temperature data in other times section into
Row adjustment, keeps other times section equal with basic crowd's temperature of temperature highest period, after adjusting face temperature data, to institute
There is face temperature data to be ranked up, calculates the scope of subject and temperature alarming value of Temperature Distribution；
Step 3, the temperature of acquisition is modified according to obtained scope of subject and temperature alarming value.
Preferably, the method also includes, before step 1, the first restriction area of automatic learning foundation crowd's temperature acquisition
Domain, in step 1, based on acquisition passenger's face temperature in limited area.
Wherein, the limited area of the automatic learning foundation crowd temperature acquisition specifically includes:
The infrared image of acquisition is using black matrix as benchmark, and continuous acquisition full figure is no more than black body radiation face lower boundary height
Infrared image；Face temperature data and corresponding infrared image coordinate position are extracted from infrared image；
It counts nDay days face temperature datas and all face temperature was calculated according to nDay days of statistics face temperature datas
The mean value Avgtemp of degree evidence sorts the absolute value of all face temperature datas from small to large, and retening temperature is near samming
The m temperature data of Avgtemp is as temperature samples data；The trigger condition of the calculating is system boot starting, wherein m=
FR1* total face temperature data quantity；
Infrared image region is divided equally into n rectangular area, such as that infrared image 640*480 is divided into 32*24 is big
Small image block amounts to 400 rectangular areas, counts the quantity nLocalNum of temperature samples data in each rectangular area, sentence
Whether the quantity of all temperature samples data in disconnected rectangular area accounts for the fR2=of the total face temperature data quantity in the region
80% or more, if it is, the rectangular area to be retained as to the stability region of temperature acquisition；
Above-mentioned institute stability region with a grain of salt is merged, binaryzation template is saved as, is used as basic crowd's temperature acquisition
Limited area.
Experimental stage at the scene carried out above-mentioned calculating daily, is learnt automatically since second day, compared automatic learn
Gained limited area is practised with the presence or absence of variation.
Wherein, described nDay days are adjustable parameter, default value 5 counts 5 days face temperature datas；The fR1 is
Adjustable scale parameter, default 90% retain hithermost with samming Avgtemp that is, from locking in all face temperature datas
90% temperature data is as temperature samples data, such as has 1000 temperature datas, then therefrom takes near mean value
1000*90%=900 temperature data is as temperature samples data；FR2 be can ratio tune parameter, default 80%, that is, judge
Whether the quantity of all temperature samples data in rectangular area accounts for 80% or more of the total face temperature data quantity in the region；
Wherein, acquisition time is accurate to the second.
The present invention proposes a kind of infrared survey human body temperature modification method, can be based on the automatic study body temperature sample of black body locus
The limited area of this acquisition excludes the interference in other regions, and based on the temperature samples data statistics acquired in limited area point
Analysis, is segmented the darg time according to the variation of temperature, calculates basic crowd's temperature of each period, calculates temperature point
The scope of subject and temperature alarming value of cloth, and obtain the temperature correction of shell temperature mapping relations.
Preferably, step 1 specifically includes:
At interval of the temperature data of all faces in the limited area of acquisition in nSec seconds, including face temperature data pair
The infrared image coordinate and acquisition time answered.
Each hour as a cycle, calculates in the darg period, all faces acquired in section per hour
Temperature mean of mean nTempMeanPerHour saves this day calculated all average value nTempMeanPerHour and makees
For 1 group of temperature mean data；The temperature mean data includes the face temperature mean value of all small periods in the darg time
Average value, such as 1 day working time was 24 hours, calculates a mean value per hour, is i.e. includes in 1 group of temperature mean data
24 temperature mean value nTempMeanPerHour correspond to a temperature mean value nTempMeanPerHour per hour.
When daily system boot operation, from current date nToday to being pushed forward nNearDays days, the nNearDays is extracted
It temperature mean data, i.e. extraction n group temperature mean data；
Merging n group temperature mean data will be identical small in n group temperature mean data as new one group of temperature mean data
When the period the mean value of n nTempMeanPerHour the temperature of hour period is corresponded to as new one group of temperature mean data
Mean value, such as calculate the average value work of respective 1st hour temperature mean value nTempMeanPerHour in n group temperature mean value number
For the 1st hour temperature mean value nTempMeanPerHour of new one group of temperature mean data；
Find maximum temperature mean value fTempMeanHigh and minimum equal temperature mean value in new one group of temperature mean data
FTempMeanLow, calculating temperature difference fTempMeanDiff=fTempMeanHigh-fTempMeanLow；
If temperature difference fTempMeanDiff≤temperature difference preset value fTempThresh, do not need to draw the darg time
It is divided into different time sections, does not need to carry out period amendment；
If temperature difference fTempMeanDiff > temperature difference preset value fTempThresh, with (fTempMeanHigh+
FTempMeanLow)/2 as the fragmentation threshold fTimeSegThresh of temperature-time section, the darg time is divided into 3
The continuous time period of period, i.e. temperature mean value lower than fTimeSegThresh is as first time period, by temperature mean value
Temperature mean value will be lower than fTimeSegThresh as second time period by the continuous time period higher than fTimeSegThresh
Another continuous time period as the third period；
By the above method, the darg time is divided into three sections, such as morning, noon, afternoon, the temperature of noon section
The temperature in degree highest, morning and afternoon is below noon temperature.
The mean value of all face temperature datas of closest nNearDays days same time periods is calculated, when as dividing
Between each period after section basic crowd's temperature, such as the basis of first time period, second time period and third period
Crowd's temperature is respectively M1, M0 and M2.
Wherein, fTempThresh is adjustable parameter, defaults 0.2 DEG C, nNearDays defaults 5, fR3 and defaults 10%.fR4
It is defaulted as 2 ‰.
In above-described embodiment, the case where being not at single small period starting point when to booting, be more than (containing) 0.5 hour it is then single
Temperature mean value is solely calculated, data were then included into neighbouring hour calculating temperature mean value no more than 0.5 hour, wherein total people
Number is no less than numPeople people, adjustable parameter, and numPeople default 1000 can adjust according to field condition；Each hour
As a cycle, last second (XX:59:59) triggering hourly is calculated, and calculates all face temperature mean values in one hour
Average value nTempMeanPerHour.
Preferably, step 2 specifically includes:
According to basic crowd's temperature of each period obtained, basis crowd's temperature highest period is determined,
As the highest temperature period, other times section and basic crowd's temperature gap Dvalue between the highest temperature period are calculated, it will
All face temperature datas in other times section add corresponding basic crowd temperature gap Dvalue, so that other times
Basic crowd's temperature of section is equal with basic crowd's temperature of highest temperature period；
All face temperature datas in other times section to the highest temperature period and after drawing high are ranked up, and are obtained
The scope of subject [fMinTemp, fMaxTemp] and temperature alarming value fAlarmTempIr of Temperature Distribution；Wherein
FMinTemp is that maximum fR3 face temperature data and the smallest fR3 face are removed from the face temperature data of sequence
Minimum value after temperature data in remaining face temperature data, fMaxTemp are to remove from the face temperature data of sequence
Minimum after maximum fR3 face temperature data and the smallest fR3 face temperature data in remaining face temperature data
Value, fAlarmTempIr are the remaining people after removing maximum fR4 face temperature data in the face temperature data of sequence
Maximum value in face temperature data.
Wherein, fR3 and fR4 is adjustable scale parameter, and fR3 is defaulted as 10%, fR4 and is defaulted as 2 ‰.
Preferably, the step 3 specifically includes:
Scope of subject and temperature alarming value based on acquisition, according to following formula to passenger's face temperature
FIrFaceTemp is mapped, and is obtained and is mapped compensated temperature fMaxppingTemp, and temperature adjustmemt is completed；
Wherein the calculating of temperature range compressed coefficient fCoef is as follows:
FCoef1=min (1, [(36.5-36.0)/(fMaxTempMean-fMinTempMean)])；
FCoef2=min (1, [(37.3-36.5)/(fAlarmTempIr-fMaxTempMean)])；
Wherein fMinTemp is the minimum value in the scope of subject of Temperature Distribution, and fMaxTemp is the main body model of Temperature Distribution
Maximum value in enclosing, fAlarmTempIr are temperature alarming value.
By above-mentioned formula, the temperature after can mapping through body temperature range [fMinTemp, fMaxTemp] is controlled
Within the scope of 36.0~36.5 DEG C；Temperature map by temperature lower than fMinTempMean is to 36.0 DEG C；Will (fMaxTemp,
FAlarmTempIr] be mapped to (365,37.3]；The temperature that will be above temperature fAlarmTempIr is compressed according to temperature range is
Number Coef2 is mapped to 37.3 DEG C or more.
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (8)
1. a kind of infrared survey human body temperature modification method, which is characterized in that the described method includes:
Step 1, passenger's face temperature data is acquired, passenger's face temperature data of acquisition is analyzed, judges whether to need
The darg time is divided into different time sections, if judgement needs darg time being divided into different time sections, is calculated
Basis crowd's temperature in different time periods；
Step 2, it determines basic crowd's temperature highest period, the face temperature data in other times section is adjusted
It is whole, keep other times section equal with basic crowd's temperature of temperature highest period, after adjusting face temperature data, to owner
Face temperature data is ranked up, and calculates the scope of subject and temperature alarming value of Temperature Distribution；
Step 3, the temperature of acquisition is modified according to obtained scope of subject and temperature alarming value.
2. infrared survey human body temperature modification method according to claim 1, which is characterized in that the method also includes,
Before step 1, the first limited area of automatic learning foundation crowd's temperature acquisition, in step 1, based on being adopted within the scope of limited area
Collect passenger's face temperature.
3. infrared survey human body temperature modification method according to claim 2, which is characterized in that the automatic learning foundation
The limited area of crowd's temperature acquisition specifically includes:
Continuous acquisition infrared image extracts face temperature data and corresponding infrared image coordinate position from infrared image；
It counts nDay days face temperature datas and all face temperature was calculated according to nDay days of statistics face temperature datas
Mean value Avgtemp, retening temperature near samming Avgtemp m face temperature data as temperature samples data, wherein m
=fR1* total face temperature data quantity；
Infrared image region is divided equally into n rectangular area, counts the quantity of temperature samples data in each rectangular area
NLocalNum, judges whether the quantity of all temperature samples data in rectangular area accounts for the total face temperature data in the region
The fR2 or more of quantity, if it is, the rectangular area to be retained as to the stability region of temperature acquisition；
Above-mentioned institute stability region with a grain of salt is merged, binaryzation template is saved as, is used as the restriction of basic crowd's temperature acquisition
Region.
4. infrared survey human body temperature modification method according to claim 2, which is characterized in that the infrared image of acquisition is
Using black matrix as benchmark, acquisition full figure is no more than the infrared image of black body radiation face lower boundary height.
5. infrared survey human body temperature modification method according to claim 1, which is characterized in that step 1 specifically includes:
The temperature data of primary all faces was acquired at interval of nSec seconds；
Each hour as a cycle, calculates all face temperature means of mean acquired in each small period
NTempMeanPerHour saves this day calculated all average value nTempMeanPerHour as 1 group of temperature mean value number
According to；
When daily system boot operation, from current date nToday to being pushed forward nNearDays days, extract this nNearDays days
Temperature mean data, i.e. extraction n group temperature mean data；
Merge n group temperature mean data, it, will be in n group temperature mean data when identical hour as new one group of temperature mean data
Between section n nTempMeanPerHour the mean value temperature that corresponds to hour period as new one group of temperature mean data it is equal
Value；
Find maximum temperature mean value fTempMeanHigh and minimum equal temperature mean value in new one group of temperature mean data
FTempMeanLow, calculating temperature difference fTempMeanDiff=fTempMeanHigh-fTempMeanLow；
If temperature difference fTempMeanDiff > temperature difference preset value fTempThresh, with (fTempMeanHigh+
FTempMeanLow)/2 as the fragmentation threshold fTimeSegThresh of temperature-time section, when the darg time is divided into 3
Between section, i.e. a continuous time period of the temperature mean value lower than fTimeSegThresh is used as first time period, by temperature mean value height
In fTimeSegThresh continuous time period as second time period, will be another lower than fTimeSegThresh by temperature mean value
One continuous time period is as the third period；
Calculate the mean value of all face temperature datas of closest nNearDays days same time periods, as sliced time section
Basic crowd's temperature of each period afterwards.
6. infrared survey human body temperature modification method according to claim 5, which is characterized in that if the temperature difference
FTempMeanDiff≤temperature difference preset value fTempThresh, then do not need darg time being divided into different time sections,
It does not need to carry out period amendment.
7. infrared survey human body temperature modification method according to claim 1, which is characterized in that step 2 specifically includes:
According to basic crowd's temperature of each period obtained, basis crowd's temperature highest period is determined, as
The highest temperature period calculates other times section and basic crowd's temperature gap Dvalue between the highest temperature period, by other
All face temperature datas in period add corresponding basic crowd temperature gap Dvalue, so that other times section
Basic crowd's temperature is equal with basic crowd's temperature of highest temperature period；
All face temperature datas in other times section to the highest temperature period and after drawing high are ranked up, and obtain temperature
The scope of subject [fMinTemp, fMaxTemp] and temperature alarming value fAlarmTempIr of distribution；Wherein fMinTemp be from
It is remained after removing maximum fR3 face temperature data and the smallest fR3 face temperature data in the face temperature data of sequence
Minimum value in remaining face temperature data, fMaxTemp are that maximum fR3 people is removed from the face temperature data of sequence
Minimum value after face temperature data and the smallest fR3 face temperature data in remaining face temperature data,
FAlarmTempIr is the remaining face temperature after removing maximum fR4 face temperature data in the face temperature data of sequence
Maximum value of the degree in.
8. infrared survey human body temperature modification method according to claim 1, which is characterized in that the step 3 is specifically wrapped
It includes:
Scope of subject and temperature alarming value based on acquisition, according to following formula to passenger face temperature fIrFaceTemp into
Row mapping, obtains and maps compensated temperature fMappingTemp, completes temperature adjustmemt；
Wherein the calculating of temperature range compressed coefficient fCoef is as follows:
FCoef1=min (1, [36.5-36.0)/(fMaxTempMean-fMinTempMean)])；
FCoef2=min (1, [37.3-36.5)/(fAlarmTempIr-fMaxTempMean)])；
Wherein fMinTempm is the minimum value in the scope of subject of Temperature Distribution, and fMaxTempm is the scope of subject of Temperature Distribution
In maximum value, fAlarmTempIr be temperature alarming value.
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