CN107133605A - A kind of train operator's monitoring system and device based on LTE R networks - Google Patents
A kind of train operator's monitoring system and device based on LTE R networks Download PDFInfo
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- CN107133605A CN107133605A CN201710379848.3A CN201710379848A CN107133605A CN 107133605 A CN107133605 A CN 107133605A CN 201710379848 A CN201710379848 A CN 201710379848A CN 107133605 A CN107133605 A CN 107133605A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
- G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/30—Noise filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
- G06V40/164—Detection; Localisation; Normalisation using holistic features
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/18—Eye characteristics, e.g. of the iris
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/24—Reminder alarms, e.g. anti-loss alarms
Abstract
A kind of train operator's monitoring system based on LTE R networks, it is characterized in that, monitoring system includes the real-time monitoring identifier and the controller positioned at train controlling center server end positioned at train driving room client, monitoring identifier is provided with the camera for possessing automatic focusing function in real time, the camera constantly obtains train operator's face image with default frequency after train operation, and monitoring identifier is handled train operator's face image in real time；Think that train operator is in when monitoring identifier in real time to leave, sleep and during absent-minded state, by controller of the real-time video by LTE R network transmissions to server end, by the working condition of artificial judgment driver, and be made whether to need the judgement of early warning, judged result sends back the real-time monitoring identifier of client by LTE R networks；The judged result that the real-time monitoring identifier of client is transmitted according to the controller of service end, when needing early warning, early warning is sent to driver.
Description
Technical field
The invention belongs to train security technology area, more particularly to a kind of train operator's monitoring system based on LTE-R networks
System and device.
Background technology
With continuing to develop for the communications industry, LTE network has been stepped into the daily life of masses, and LTE-R networks
It is also the developing direction in following railway communication field.How LTE-R networks utilize its number while core control business is completed
According to function, it is following research direction to realize broader practice.
To ensure train driving safety, it is necessary to which the working condition to train operator is monitored and recognized.At present, on train
A kind of hardware device of pedal form is widely used, situations such as to recognize whether driver leaves, sleep.Although this equipment
Realize pre-provisioning request well in practice, but be due to inborn defect, this kind equipment can not be also realized from control centre
Monitoring in real time, control and the function of reminding driver, and LTE-R networks can play its potentiality in this respect, make up existing equipment
Defect.
LTE-R technologies are a kind of based on LTE, are the network that railway communication is set up.3GPP R13 define LTE-R core
Heart technology, has used the technological means such as orthogonal frequency-time multiple access technology (OFDMA) and multichannel turnover technology (MIMO), it has
The data-transformation facility of high speed, service rate disclosure satisfy that the requirement of transmission of video more than 100Mb/s.
The content of the invention
The present invention provides a kind of train operator's monitoring system and device based on LTE-R networks, is existed with adapting to LTE-R networks
Application in Train Detection and Identification, lifts safe train operation control technology grade.
A kind of train operator's monitoring system based on LTE-R networks, monitoring system includes being located at train driving room client
Real-time monitoring identifier and the controller positioned at train controlling center server end, including step：
Monitoring identifier is provided with the camera for possessing automatic focusing function in real time, and the camera is with default after train operation
Frequency constantly obtains train operator's face image, and monitoring identifier is handled train operator's face image in real time；
Think that train operator is in when monitoring identifier in real time to leave, sleep and during absent-minded state, real-time video is passed through
LTE-R network transmissions by the working condition of artificial judgment driver, and be made whether to need early warning to the controller of server end
Judge, judged result sends back the real-time monitoring identifier of client by LTE-R networks；
The judged result that the real-time monitoring identifier of client is transmitted according to the controller of service end, when needing early warning,
Early warning is sent to driver.
Also include step, after monitoring identifier is handled train operator's face image in real time, calculate train department
Machine eyeball and distance relation of the canthus under waking state；
When continuous multiple frames do not detect train operator's face, then it is assumed that driver is in leave state；
Recognized when for a long time less than train operator's iris, but when can recognize train operator's nose and the corners of the mouth, then it is assumed that
In closed-eye state, it is believed that driver is in sleep state；If simply indivedual discrete frames can't detect iris, then it is assumed that
Driver is blink, non-sleep state.
When discovery train operator or so eyeball does not change for a long time for the relative position at canthus, then it is assumed that do not have
Detect eye to move, then it is assumed that driver is in absent-minded state.
Long-time described here refers to a duration, and a reasonable time of the duration beyond people when regaining consciousness is long
Degree.
The processing that monitoring identifier is carried out to train operator's face image in real time includes image preprocessing, and image is located in advance
Reason includes step：
Filtering and noise reduction sound, reduction grass carrys out distortion and degraded；
Dimension normalization, unified standard size image is transformed in order to face by the different facial image of size
The extraction of feature；
Gray scale normalization, for colored face picture, the processing of row gray processing；
Grayscale equalization, grayscale equalization processing is carried out to facial image, and its effect is that the entirety of enhancing facial image is right
Than degree, and make intensity profile uniform, to eliminate the influence of illumination variation.
The processing that monitoring identifier is carried out to train operator's face image in real time also includes：Face characteristic is extracted, and is carried
Take out the left and right canthus, eyeball center, nose and corners of the mouth position of two eyes of face.
Monitoring identifier calculates distance of described two eyeballs of train operator with respect to left eye angle in real time.
Camera obtains the face image of train operator with fixed frame per second.
The face image for the train operator that monitoring identifier is obtained to camera carries out Face Detection in real time, and interception has skin
The facial image of color.
A kind of train operator's supervising device based on LTE-R networks, the supervising device is one and is located at train driving room
Monitoring identifier, monitors identifier and is provided with the camera for possessing automatic focusing function in real time in real time,
Monitoring identifier performs following steps in real time after train operation：
Control camera constantly obtains train operator's face image with default frequency, and monitoring identifier is to the row in real time
Car driver's face image is handled；Predeterminated frequency suggestion is higher than 5 frame per second.
Think that train operator is in when monitoring identifier in real time to leave, sleep and during absent-minded state, real-time video is passed through
LTE-R network transmissions by the working condition of artificial judgment driver, and are made to the controller positioned at train controlling center server end
Go out whether to need the judgement of early warning, judged result sends back the real-time monitoring identifier of client by LTE-R networks；
The judged result that the real-time monitoring identifier of client is transmitted according to the controller of service end, when needing early warning,
Early warning is sent to driver.
After monitoring identifier is handled train operator's face image in real time, train operator's eyeball and canthus are calculated
Distance relation under waking state；
When continuous multiple frames do not detect train operator's face, then it is assumed that driver is in leave state；
Recognized when for a long time less than train operator's iris, but when can recognize train operator's nose and the corners of the mouth, then it is assumed that
In closed-eye state, it is believed that driver is in sleep state；If simply indivedual discrete frames can't detect iris, then it is assumed that
Driver is blink, non-sleep state.
When discovery train operator or so eyeball does not change for a long time for the relative position at canthus, then it is assumed that do not have
Detect eye to move, then it is assumed that driver is in absent-minded state.
Long-time described here refers to a duration, and a reasonable time of the duration beyond people when regaining consciousness is long
Degree.
The processing that monitoring identifier is carried out to train operator's face image in real time includes image preprocessing, and image is located in advance
Reason includes step：
Filtering and noise reduction sound, reduction grass carrys out distortion and degraded；
Dimension normalization, unified standard size image is transformed in order to face by the different facial image of size
The extraction of feature；
Gray scale normalization, for colored face picture, the processing of row gray processing；
Grayscale equalization, grayscale equalization processing is carried out to facial image, and its effect is that the entirety of enhancing facial image is right
Than degree, and make intensity profile uniform, to eliminate the influence of illumination variation.
Eye is dynamic to be judged
Blink judges：
The technical scheme is that using the camera on train operation platform, driver working state is monitored in real time, according to department
Whether the eye movement data of machine, abnormal blink situation identification driver sleep or absent-minded.If it is determined that display driver's abnormal state, is
The real-time video for then obtaining and sending driver unite to server, control centre's manual identified is transferred to.According to result manual identified knot
Really, it is determined whether send the signal for reminding driver.Highspeed Data Transmission Technology and recognition of face skill of the present invention based on LTE-R
Art, there is provided real time data during by locally judging when the risk of the dangerous operation of driver, secondary-confirmation is carried out by control centre.
The present invention is based on face recognition technology, and based on following knowledge：
1) monitor camera can monitor whole operation platform, when driver does not leave operating desk, and the face of driver is all
It can appear on monitored picture；
2) when people during sleep, eyelid is in closure state, and certain time, and the duration is more than 10s.It is exactly
Say when driver's eyelid is in closure state more than 10s, then it is assumed that driver is likely to be at sleep state.
3) when people sees thing in normal state, iris does not always stop change relative to canthus position, if
Relative position changes, then it is assumed that eyes have normal eyes and moved.If left and right eyeball exceedes relative to the position at left and right canthus
, may be absent-minded when certain time keeps constant, then it is assumed that do not detect eye and move.
The advantageous effects of the present invention include：
1. using face recognition technology, judge the actions such as the dynamic, blink of eye, improve the accuracy of identification；
2. carrying out secondary-confirmation by control centre, accuracy is improved；
3. there is provided real-time for video data real-time Transmission；
4. not increasing extra hardware, cost is advantageously reduced.
Brief description of the drawings
Fig. 1 be in the embodiment of the present invention system for train operator's face image processing flow chart.
Embodiment
It is described further below in conjunction with 1 pair of embodiment of the invention of accompanying drawing：
Present system is divided into monitoring identification end (client) and control end (server end) in real time.Client has high definition
Camera, being capable of auto-focusing.Client is automatically opened up in train operation.Service end has a display, and keyboard and mouse etc. is defeated
Enter equipment.Server end is in normally open.
Client camera obtains attendant's face data with fixed frame per second.Client software is fixed by Face datection
Position, after facial image pretreatment, extracts canthus coordinate, eyeball centre coordinate, and calculates eyeball and closed with respect to the distance at left eye angle
System；If Face datection fails, it is believed that driver leaves.If it exceeds certain time identification is less than eyeball, but it can recognize
Nose, the corners of the mouth, then it is believed that driver is in sleep state.
When client detects face, auto-focusing shoots photo or video.Then photographic data is recognized frame by frame, is carried out
Face Detection, face parts of images of the interception with the colour of skin, according to geometric properties, carries out Face datection positioning.If face is examined
Dendrometry loses, then it is believed that driver leaves.
Client includes to the pretreatment of driver's facial image：
Filtering and noise reduction sound, reduction grass carrys out distortion and degraded；
Dimension normalization, its thought be by the different facial image of size be transformed to unified standard size image with
It is easy to the extraction of face characteristic；
Gray scale normalization, for colored face picture, gray processing processing is carried out to it；Grayscale equalization, due to being adopted in image
Concentrate the change of illumination to be easily caused image and different bright-dark degrees are presented, therefore need to carry out grayscale equalization to facial image
Processing.Grayscale equalization, it is to strengthen the overall contrast of facial image that it, which is acted on, and makes intensity profile uniform, to eliminate illumination
The influence of change.
Client includes to driver's face image processing：
Face characteristic is extracted：Extract the left and right canthus of two eyes, eyeball center, nose, corners of the mouth etc.；
Record distance relation of two eyeballs with respect to left eye angle；
If it exceeds certain time identification is less than eyeball, but nose, the corners of the mouth, then it is believed that driver is in sleep can be recognized
State, constantly records and compares eyeball relative position.More than certain time, eyeball position does not change, then can consider
Driver is absent-minded；
When detect driver be in leave, sleep, absent-minded state when, client passes real-time video by LTE-R networks
It is delivered to service end；
Service end artificial judgment driver working state, and be made whether to need the judgement of early warning.Judged result passes through LTE-R
Network is sent to client；
Client if necessary to early warning, then sends early warning according to the judged result of service end to driver.
Claims (10)
1. a kind of train operator's monitoring system based on LTE-R networks, it is characterised in that monitoring system includes being located at train driving
The real-time monitoring identifier of room client and the controller positioned at train controlling center server end, including step：
Monitoring identifier is provided with the camera for possessing automatic focusing function in real time, and the camera is with default frequency after train operation
Train operator's face image is constantly obtained, monitoring identifier is handled train operator's face image in real time；
Think that train operator is in when monitoring identifier in real time to leave, sleep and during absent-minded state, real-time video is passed through into LTE-R
Network transmission by the working condition of artificial judgment driver, and be made whether to need the judgement of early warning to the controller of server end,
Judged result sends back the real-time monitoring identifier of client by LTE-R networks；
The judged result that the real-time monitoring identifier of client is transmitted according to the controller of service end, when needing early warning, to department
Machine sends early warning.
2. train operator's monitoring system as claimed in claim 1 based on LTE-R networks, it is characterised in that also including step,
After monitoring identifier is handled train operator's face image in real time, train operator's eyeball is calculated with canthus in clear-headed shape
Distance relation under state；
When continuous multiple frames do not detect train operator's face, then it is assumed that driver is in leave state；
Recognized when for a long time less than train operator's iris, but when can recognize train operator's nose and the corners of the mouth, then it is assumed that it is in
Closed-eye state, it is believed that driver is in sleep state；If simply indivedual discrete frames can't detect iris, then it is assumed that driver
Simply blink, non-sleep state,
When discovery train operator or so eyeball does not change for a long time for the relative position at canthus, then it is assumed that do not detect
It is dynamic to eye, then it is assumed that driver is in absent-minded state,
Long-time described here refers to a duration, a reasonable time length of the duration beyond people when regaining consciousness.
3. train operator's monitoring system as claimed in claim 1 based on LTE-R networks, it is characterised in that monitoring is known in real time
The processing that other device is carried out to train operator's face image includes image preprocessing, and image preprocessing includes step：
Filtering and noise reduction sound, reduction grass carrys out distortion and degraded；
Dimension normalization, unified standard size image is transformed in order to face characteristic by the different facial image of size
Extraction；
Gray scale normalization, for colored face picture, the processing of row gray processing；
Grayscale equalization, grayscale equalization processing is carried out to facial image, and it is to strengthen the overall contrast of facial image that it, which is acted on,
And make intensity profile uniform, to eliminate the influence of illumination variation.
4. train operator's monitoring system as claimed in claim 3 based on LTE-R networks, it is characterised in that monitoring is known in real time
The processing that other device is carried out to train operator's face image also includes：Face characteristic is extracted, and extracts two eyes of face
Left and right canthus, eyeball center, nose and corners of the mouth position.
5. train operator's monitoring system as claimed in claim 4 based on LTE-R networks, it is characterised in that monitoring is known in real time
Other device calculates distance of described two eyeballs of train operator with respect to left eye angle.
6. train operator's monitoring system as claimed in claim 1 based on LTE-R networks, it is characterised in that camera is with solid
Fixed frame per second obtains the face image of train operator.
7. train operator's monitoring system as claimed in claim 1 based on LTE-R networks, it is characterised in that monitoring is known in real time
The face image for the train operator that other device is obtained to camera carries out Face Detection, facial image of the interception with the colour of skin.
8. a kind of train operator's supervising device based on LTE-R networks, it is characterised in that the supervising device is one and is located at train
The real-time monitoring identifier of driver's cabin, monitors identifier and is provided with the camera for possessing automatic focusing function in real time,
Monitoring identifier performs following steps in real time after train operation：
Control camera constantly obtains train operator's face image with default frequency, and monitoring identifier is to the train department in real time
Machine face image is handled；
Think that train operator is in when monitoring identifier in real time to leave, sleep and during absent-minded state, real-time video is passed through into LTE-R
Network transmission is to the controller positioned at train controlling center server end, by the working condition of artificial judgment driver, and makes and be
No to need the judgement of early warning, judged result sends back the real-time monitoring identifier of client by LTE-R networks；
The judged result that the real-time monitoring identifier of client is transmitted according to the controller of service end, when needing early warning, to department
Machine sends early warning.
9. train operator's supervising device as claimed in claim 8 based on LTE-R networks, it is characterised in that monitoring is known in real time
After other device is handled train operator's face image, train operator's eyeball and distance of the canthus under waking state are calculated
Relation；
When continuous multiple frames do not detect train operator's face, then it is assumed that driver is in leave state；
Recognized when for a long time less than train operator's iris, but when can recognize train operator's nose and the corners of the mouth, then it is assumed that it is in
Closed-eye state, it is believed that driver is in sleep state；If simply indivedual discrete frames can't detect iris, then it is assumed that driver
Simply blink, non-sleep state,
When discovery train operator or so eyeball does not change for a long time for the relative position at canthus, then it is assumed that do not detect
It is dynamic to eye, then it is assumed that driver is in absent-minded state,
Long-time described here refers to a duration, a reasonable time length of the duration beyond people when regaining consciousness.
10. train operator's supervising device as claimed in claim 8 based on LTE-R networks, it is characterised in that monitoring is known in real time
The processing that other device is carried out to train operator's face image includes image preprocessing, and image preprocessing includes step：
Filtering and noise reduction sound, reduction grass carrys out distortion and degraded；
Dimension normalization, unified standard size image is transformed in order to face characteristic by the different facial image of size
Extraction；
Gray scale normalization, for colored face picture, the processing of row gray processing；
Grayscale equalization, grayscale equalization processing is carried out to facial image, and it is to strengthen the overall contrast of facial image that it, which is acted on,
And make intensity profile uniform, to eliminate the influence of illumination variation.
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Application publication date: 20170905 |