CN106408932A - Mobile terminal based pre-warning system for distractive walks - Google Patents
Mobile terminal based pre-warning system for distractive walks Download PDFInfo
- Publication number
- CN106408932A CN106408932A CN201610881571.XA CN201610881571A CN106408932A CN 106408932 A CN106408932 A CN 106408932A CN 201610881571 A CN201610881571 A CN 201610881571A CN 106408932 A CN106408932 A CN 106408932A
- Authority
- CN
- China
- Prior art keywords
- user
- mobile terminal
- sample
- unit
- decision
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000012549 training Methods 0.000 claims abstract description 53
- 238000011897 real-time detection Methods 0.000 claims abstract description 24
- 230000001771 impaired effect Effects 0.000 claims description 23
- 238000005070 sampling Methods 0.000 claims description 13
- 238000000034 method Methods 0.000 claims description 10
- 235000013399 edible fruits Nutrition 0.000 claims description 4
- 238000004380 ashing Methods 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 2
- 238000001514 detection method Methods 0.000 abstract description 14
- 238000002203 pretreatment Methods 0.000 abstract 2
- 238000012360 testing method Methods 0.000 description 4
- 238000005265 energy consumption Methods 0.000 description 3
- 241000208340 Araliaceae Species 0.000 description 2
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 2
- 235000003140 Panax quinquefolius Nutrition 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 235000008434 ginseng Nutrition 0.000 description 2
- 241000283070 Equus zebra Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 210000004247 hand Anatomy 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/005—Traffic control systems for road vehicles including pedestrian guidance indicator
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Multimedia (AREA)
- Traffic Control Systems (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention discloses a mobile terminal based pre-warning system for distractive walks, comprising a model training module and a real time detection module. The model training module conducts pre-treatment and model training based on the large amount of training data it has collected to generate a sample model for real time detection. The real time detection module, through the use of the mobile terminal, acquires the sample and conducts the pre-treatment. Based on the sample model generated by the model training module, the real time detection module conducts separate detections to the acquired samples. According to the results of the separate detections, decisions and judgments are made so that warnings can be made to a distractive user who walks out of a sidewalk. According to the invention, it is possible to make warnings to a user in advance before the user walks towards a dangerous zone. Without extra hardware arrangement, effective warnings can be made to the mobile terminal of a distractive walker, and the previous detection results can be effectively utilized. With high accuracy, the pre-warning system consumes less energy and can make judgments in advance.
Description
Technical field
The present invention relates to walking security fields of diverting one's attention, especially a kind of " walking of diverting one's attention " early warning system based on mobile terminal
System.
Background technology
In actual life, pedestrian's wholeheartedly dual-purpose, that is, the time-division heart of walking is sent short messages on mobile phone, makes a phone call etc. thus causing peace
The phenomenon of full problem is frequent all the more.One research coming from Stony Brook University SUNY is pointed out, diverts one's attention to send out short during walking
The people of letter 60% probability more than the people of normal walking meets with vehicle accident.A report of N.S.C.
Point out, between 2000 to 2011, pedestrian because mobile phone divert attention in accident estimated cause altogether injured more than 10,000 people.Prior art
In, continued to bring out using the technology that the various sensors on mobile terminal solve this problem, such as judge position using GPS, profit
Judge mobile phone state with accelerator, gyroscope, judge car etc. using mike, but all there is certain defect, such as precision
Not high, power consumption etc..Using the photographic head of mobile terminal, whether photographic head is photographed with vehicle to judge the whether safe skill of pedestrian
Art, does not still possess accuracy rate high and leave your defect too short to the time of user after sending alarm for.
Content of the invention
The technical problem to be solved is, provides a kind of " walking of diverting one's attention " early warning system based on mobile terminal
System, it is possible to achieve safe early warning.
For solving above-mentioned technical problem, the present invention provides a kind of " walking of diverting one's attention " early warning system based on mobile terminal, bag
Include model training module and real-time detection module;Model training module is by carrying out pre- place to a large amount of roadway characteristic training datasets
Reason and model training, generate sample pattern and are used for real-time detection;Real-time detection module carries out sampling and pre- place using mobile terminal
Reason, for obvious road markings feature, carries out classification according to the sample pattern that model training module generates to the sample of collection pre-
Survey, decision-making judgement is carried out according to the result of classification prediction, when the user judging to divert one's attention walks out footpath, user is carried out pre-
Alert.
Preferably, model training module includes pretreatment unit, model training unit and sample pattern, and pretreatment unit is used
In a large amount of training datasets are carried out with pretreatment, the data that model training unit is used for pretreatment unit is processed carries out model instruction
Practice thus producing sample pattern;Real-time detection model includes sample collection unit, pretreatment unit, classification predicting unit, decision-making
Unit and user's prewarning unit, sample collection unit is used for sampling, and pretreatment unit is used for carrying out pretreatment to the sample of collection,
Classification predicting unit is used for, according to the sample pattern that model training module generates, the sample of collection is carried out with classification prediction, decision-making list
Unit carries out decision-making judgement thus carrying out early warning by user's prewarning unit to user for the result that classification is predicted.
Preferably, the target of system detectio is sidewalk for visually impaired people, and training dataset simulation pedestrian go out, and is completed by post-positioned pick-up head
The collection of data, there is sidewalk for visually impaired people path and there is not sidewalk for visually impaired people path in image in the data of collection in image.
Preferably, the pretreatment unit in model training module carries out pretreatment to a large amount of training datasets, including ashing
It is considered to multiple parameter values, the model that different parameters are generated is tested for image and adjustment of image size, select run time and
The all preferable model of accuracy rate, records corresponding parameter value.
Preferably, the post-positioned pick-up head of the mobile terminal that sample collection unit is used based on user, photographic head is transported on backstage
Under the control of mobile phone application in row, each cycle of operation start start and obtain the image in now photographic head, will
Image sends the pretreatment unit in real-time detection module to.
Preferably, in each cycle of operation, the acceptance of decision-making judging unit is single to predict the outcome, and determines receiving this in advance
After surveying result, provide whether user walks the judgement in the footpath with sidewalk for visually impaired people;Made using predicting the outcome of multiple image
Judge, predicting the outcome and the front record predicting the outcome several times using this cycle of operation;The result of decision-making is 3 kinds:The positive, user
It is in the footpath with sidewalk for visually impaired people, enter next cycle of operation after time interval T;Feminine gender, user is not in sidewalk for visually impaired people
In footpath, enter next cycle of operation after time interval T;No, do not provide user's current state, directly sample again,
Enter next cycle of operation.
Preferably, the step of decision-making is:
(1) set the value of Treq, the usual value of Treq 1~3, and to initialize the value of intermediate variable Tdet be zero;
(2) this cycle of operation predict the outcome as Pnow, if knot has had been made in a upper cycle of operation
The judgement of fruit, and judged result Rprv is identical with Pnow, then the decision-making of this cycle of operation remains as Rprv, in time interval
The step (2) of next cycle of operation is entered after T;
(3) if Rprv is different from Pnow, then judge the Pprv whether phase that predicts the outcome of Pnow and previous cycle of operation
With;If identical, then make the value of Tdet increase by 1, and enter next step;If it is different, then the value of Tdet is entered as zero, when secondary
The result of decision be no, then immediately proceed to the step (2) of next cycle of operation;
(4) judge whether the value of Tdet is not less than the value of Treq, if so, then the result of decision of this cycle of operation is
Pnow;If it is not, when the secondary result of decision is no, then entering the step (2) of next cycle of operation after time interval T.
Beneficial effects of the present invention are:When user's handheld mobile phone moves towards driveway from footpath, oblique due to holding mobile phone
Under angle, and hold the presence of mobile phone height, the picture of mobile phone camera capture is by for the road surface in front of user, the system energy
Enough just provide warning before the deathtrap of user aisle in advance;On the basis of customizing without additional hardware, can be to " step of diverting one's attention
Mobile terminal user OK " carries out effective early warning, and front testing result several times can be carried out with effectively utilizes, and accuracy is high, consumption
Can be low, can be judged in advance.
Brief description
Fig. 1 is the overall system structure schematic diagram of the present invention.
Fig. 2 is the decision method schematic flow sheet of the present invention.
Fig. 3 is early warning scene graph during user's use of the present invention.
Specific embodiment
As shown in figure 1, a kind of " walking of diverting one's attention " early warning system based on mobile terminal, including model training module with real time
Detection module;Model training module, by a large amount of training datasets are carried out with pretreatment and model training, generates sample pattern and uses
In real-time detection;Real-time detection module is sampled using mobile terminal and pretreatment, the sample being generated according to model training module
This model carries out classification prediction to the sample of collection, carries out decision-making judgement according to the result of classification prediction, the use divert one's attention when judgement
When footpath is walked out at family, early warning is carried out to user.
The target of system detectio is sidewalk for visually impaired people, and when training dataset is gone out by simulating pedestrian, walking is while use handss
Machine, completes by post-positioned pick-up head to gather.The data of collection is classified into two classes, and a class is to there is sidewalk for visually impaired people path in image,
One class is to there is not sidewalk for visually impaired people.Two kinds of data correspond to safety zone and the insecure area judgement of decision-making judged result, once
Correct decision-making judges that the image that can there will be sidewalk for visually impaired people is judged as safety zone, and the judgement that there will be no sidewalk for visually impaired people path is non-peace
Region-wide.
Training dataset is carried out pretreatment by model training module first, including being ashed image and adjustment of image size, so
It is trained setting up model with pretreated data afterwards.In this course it will be considered that multiple parameter values, including adjusting image
Size, classifier type of selection etc..Then to different parameters generate model test, select run time and
The all comparatively ideal model of accuracy rate, notes down corresponding parameter value, for the real-time detection on mobile terminal.By image procossing
With dynamic sampling characteristic, improve the corresponding real-time of system;By Image semantic classification, including ashing image and adjustment of image size,
From the sorter model that run time and accuracy rate are all more satisfactory, improve response speed.
After collecting sufficient amount training dataset image, pre- by image is carried out according to the parameter of setting to training data
Process, process step includes being ashed image and adjustment of image size, being provided with parameter is picture size size.Then model instruction
Practicing unit will be based on pretreated training dataset image, and the parameter according to setting carries out model training, sets up for real-time
The sample pattern of the classification predicting unit of detection module, the parameter being provided with includes selecting in the classifier type adopting and image
Feature point number.Set up after sample pattern according to these parameters, model training unit also will be carried out to the model establishing
Adjust ginseng work, set up after different sample patterns using part training sample according to different parameter value collocation at random, so
Using this model, other untapped training datasets are carried out with test prediction afterwards, veritify and predict the outcome and legitimate reading whether
Show and calculate predictablity rate, record the time-consuming of this prediction process simultaneously.Count the accuracy rate of different sample patterns with
Time-consuming result, then from going out run time and all comparatively ideal sample pattern of accuracy rate, using this model corresponding image chi
The parameter value of very little size, classifier type and feature point number is for setting up the final sample pattern using.
The post-positioned pick-up head of the mobile terminal that the sample collection unit in real-time detection module is used based on user.Photographic head
By in running background mobile phone application control under, each cycle of operation start most start and capture now photographic head
In picture, image is sent to pretreatment unit.
The single image that pretreatment unit in each cycle of operation, real-time detection module is obtained carries out pre- place
Reason, preprocess method is consistent with preprocess method in model training module.
Classification predicting unit in each cycle of operation, real-time detection module will be selected according to from model training module
Model and parameter, come to this unit obtain pretreated image carry out classification prediction, to judge whether comprise in image
Sidewalk for visually impaired people, predicts the outcome and will send to decision package.
As shown in Fig. 2 the decision package in each cycle of operation, real-time detection module predicts the outcome single for acceptance,
And determine receiving after this predicts the outcome, the judgement of current environment can be provided, that is, whether user walks in the People's Bank of China with sidewalk for visually impaired people
Judgement on road.Decision thought is, the prediction not entirely accurate of single image, but if the prediction knot using multiple image
Fruit judging, the record predicting the outcome several times before using in other words, then the judging nicety rate made will greatly promote.
The foundation of decision-making predicting the outcome and the front record predicting the outcome several times for this cycle of operation.The result of decision-making is 3 kinds:Positive
(user is in the footpath with sidewalk for visually impaired people, enters next cycle of operation after time interval T), negative (user is not in carrying
In the footpath of sidewalk for visually impaired people, enter next cycle of operation after time interval T), no (do not provide user's current state, directly again
Secondary sampling, enters next cycle of operation).The step of decision-making is:
(1) set the value (Treq value is usually 1~3) of Treq, its meaning is to consider this basis predicting the outcome
On, this decision-making also to be considered before the predicting the outcome of Treq cycle of operation.And initialize intermediate variable Tdet's
Value is zero, and its meaning is to judge whether the number that predicts the outcome currently considered meets Treq and require.
(2) this cycle of operation predict the outcome as Pnow, if knot has had been made in a upper cycle of operation
The judgement of fruit, and judged result Rprv is identical with Pnow, then the decision-making of this cycle of operation remains as Rprv, in time interval
The step (2) of next cycle of operation is entered after T.
(3) if Rprv is different from Pnow, then judge the Pprv whether phase that predicts the outcome of Pnow and previous cycle of operation
With.If identical, then make the value of Tdet increase by 1, and enter next step;If it is different, then the value of Tdet is entered as zero, when secondary
The result of decision be no, then immediately proceed to the step (2) of next cycle of operation.
(4) judge whether the value of Tdet is not less than the value of Treq, if so, then the result of decision of this cycle of operation is
Pnow;If it is not, when the secondary result of decision is no, then entering the step (2) of next cycle of operation after time interval T.
As shown in figure 3, when user's handheld mobile phone moves towards driveway from footpath, due to holding mobile phone angle obliquely,
And hold the presence of mobile phone height, the picture of mobile phone camera capture is by for the road surface in front of user.Also because this feature, this
System just can provide warning before user goes to deathtrap in advance.
The present invention adopts dynamic sampling interval and photaesthesia feature, mitigates system energy consumption expense, and reserved user is more tight
The response time of anxious situation.System will be according to user's run trace, if user goes on along in safety zone, system will suitably increase
Plus the sampling interval, reduce system energy consumption, but constrain the maximum sampling interval simultaneously, it is to avoid the sampling interval infinitely expands, and reduces system
Safety.If user passes through often walks in dangerous region, system suitably will reduce the sampling interval, enable a system to preferably
Find dangerous situation, give user warning.In the scene that night or light are bad, if user uses mobile phone by extreme influence row
Road safety, and system nor judge safety, will quit work and persistently report to the police, this operation can reduce system energy consumption with
When ensure user security.And be also the mistake in an important dynamic regulation sampling interval of the system in above-mentioned decision method
Journey, when the detection of system has determined different non-footpath and judges from footpath before, system will be skipped the sampling interval,
Directly carry out sampling next time and detection.This is due to coming from two kinds of probabilities, detection mistake and user the occurrence of this
Walk out footpath it is therefore desirable to detect to draw correct judgement immediately again, if it is determined that being that user walks out footpath, then will
Provide warning, the method just can keep for user's more time to tackle emergency.
Roadway characteristic is detected by mobile terminal camera, there is wide applicability.In view of current every country is all adopted
The mode arranging label information used in road reminds pedestrian, and for example Belgium's white line on road forms and " sends short messages special
Road ".The present invention by only changing training dataset, and can not change the mode of any software, is applied to new scene, tool
There is wide applicability.
The system, when changing detection object, is applied to transfer real-time detection module during scene beyond sidewalk for visually impaired people is detected,
Only need to change the training dataset of model training module, using identical systems unit and method, image is carried out to data set and locate in advance
Manage, set up sample pattern and adjust ginseng, you can obtain the final sample model corresponding to this detection object, for real-time detection module
Use.And the difference degree that the accuracy rate of generally detection is concentrated inhomogeneity sample by training data is affected.In the system
In test, except the detection of sidewalk for visually impaired people, the detection of zebra crossing, and the detection arranging visible marking in road also have excellent standard
Really rate and detection time performance.
Although the present invention is illustrated with regard to preferred implementation and has been described, it is understood by those skilled in the art that
Without departing from scope defined by the claims of the present invention, variations and modifications can be carried out to the present invention.
Claims (7)
1. a kind of " walking of diverting one's attention " early warning system based on mobile terminal is it is characterised in that include:Model training module is with real time
Detection module;Model training module, by a large amount of roadway characteristic training datasets are carried out with pretreatment and model training, generates sample
This model is used for real-time detection;Real-time detection module is sampled using mobile terminal and pretreatment, for obvious road markings
Feature, carries out classification prediction according to the sample pattern that model training module generates to the sample of collection, according to the knot of classification prediction
Fruit carries out decision-making judgement, when the user judging to divert one's attention walks out footpath, carries out early warning to user.
2. " walking of the diverting one's attention " early warning system based on mobile terminal as claimed in claim 1 is it is characterised in that model training mould
Block includes pretreatment unit, model training unit and sample pattern, and pretreatment unit is pre- for carrying out to a large amount of training datasets
Process, the data that model training unit is used for pretreatment unit is processed carries out model training thus producing sample pattern;In real time
Detection model includes sample collection unit, pretreatment unit, classification predicting unit, decision package and user's prewarning unit, sample
Collecting unit is used for sampling, and pretreatment unit is used for the sample of collection is carried out pretreatment, and classification predicting unit is used for according to mould
The sample pattern that type training module generates carries out classification prediction to the sample of collection, and decision package is used for the result to classification prediction
Carry out decision-making judgement thus early warning is carried out to user by user's prewarning unit.
3. as claimed in claim 1 " walking of the diverting one's attention " early warning system based on mobile terminal is it is characterised in that system detectio
Target is sidewalk for visually impaired people or other roads mark feature, and training dataset simulation pedestrian go out, and completes data by post-positioned pick-up head
Collection, there is sidewalk for visually impaired people path and there is not sidewalk for visually impaired people path in image in the data of collection in image.
4. " walking of the diverting one's attention " early warning system based on mobile terminal as claimed in claim 2 is it is characterised in that model training mould
Pretreatment unit in block carries out pretreatment to a large amount of training datasets, including ashing image and adjustment of image size it is considered to many
Individual parameter value, the model that different parameters are generated is tested, and selects run time and all preferable model of accuracy rate, it is right to record
The parameter value answered.
5. " walking of the diverting one's attention " early warning system based on mobile terminal as claimed in claim 2 is it is characterised in that sample collection list
Unit based on user use mobile terminal post-positioned pick-up head, photographic head in running background mobile phone application control under,
The starting of each cycle of operation starts and obtains the image in now photographic head, and image is sent in real-time detection module
Pretreatment unit.
6. " walking of the diverting one's attention " early warning system based on mobile terminal as claimed in claim 2 is it is characterised in that run at each
Cycle, decision-making judging unit accept single predicts the outcome, and determine receiving after this predicts the outcome, provide whether user walks
Judgement in footpath with sidewalk for visually impaired people;Judged using predicting the outcome of multiple image, using the prediction of this cycle of operation
Result and the front record predicting the outcome several times;The result of decision-making is 3 kinds:The positive, user is in the footpath with sidewalk for visually impaired people, when
Between enter next cycle of operation after the T of interval;Feminine gender, user is not in the footpath with sidewalk for visually impaired people, enters after time interval T
Next cycle of operation;No, do not provide user's current state, directly sample again, enter next cycle of operation.
7. " walking of the diverting one's attention " early warning system based on mobile terminal as claimed in claim 6 is it is characterised in that the step of decision-making
For:
(1) set the value of Treq, the usual value of Treq 1~3, and to initialize the value of intermediate variable Tdet be zero;
(2) this cycle of operation predict the outcome as Pnow, if having been made resultful in a upper cycle of operation
Judge, and judged result Rprv is identical with Pnow, then the decision-making of this cycle of operation remains as Rprv, after time interval T
Enter the step (2) of next cycle of operation;
(3) if Rprv is different from Pnow, then judge whether Pnow is identical with the Pprv that predicts the outcome of previous cycle of operation;If
Identical, then to make the value of Tdet increase by 1, and enter next step;If it is different, then the value of Tdet is entered as zero, when secondary is determined
Plan result is no, then immediately proceeds to the step (2) of next cycle of operation;
(4) judge whether the value of Tdet is not less than the value of Treq, if so, then the result of decision of this cycle of operation is Pnow;If
It is not when the secondary result of decision is no, then to enter the step (2) of next cycle of operation after time interval T.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610881571.XA CN106408932B (en) | 2016-10-09 | 2016-10-09 | A kind of " walking of diverting one's attention " early warning system based on mobile terminal |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201610881571.XA CN106408932B (en) | 2016-10-09 | 2016-10-09 | A kind of " walking of diverting one's attention " early warning system based on mobile terminal |
Publications (2)
Publication Number | Publication Date |
---|---|
CN106408932A true CN106408932A (en) | 2017-02-15 |
CN106408932B CN106408932B (en) | 2018-11-16 |
Family
ID=59228338
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201610881571.XA Active CN106408932B (en) | 2016-10-09 | 2016-10-09 | A kind of " walking of diverting one's attention " early warning system based on mobile terminal |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN106408932B (en) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108524209A (en) * | 2018-03-30 | 2018-09-14 | 江西科技师范大学 | Blind-guiding method, system, readable storage medium storing program for executing and mobile terminal |
CN108960029A (en) * | 2018-03-23 | 2018-12-07 | 北京交通大学 | A kind of pedestrian diverts one's attention behavioral value method |
CN109241916A (en) * | 2018-09-12 | 2019-01-18 | 四川长虹电器股份有限公司 | A kind of system and method for pedestrian's walking safety detection based on smart phone |
Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080309468A1 (en) * | 2007-06-12 | 2008-12-18 | Greene Daniel H | Human-machine-interface (HMI) customization based on collision assessments |
CN101853399A (en) * | 2010-05-11 | 2010-10-06 | 北京航空航天大学 | Method for realizing blind road and pedestrian crossing real-time detection by utilizing computer vision technology |
CN101964054A (en) * | 2010-09-29 | 2011-02-02 | 东南大学 | Friendly track detection system based on visual processing |
CN102609724A (en) * | 2012-02-16 | 2012-07-25 | 北京航空航天大学 | Method for prompting ambient environment information by using two cameras |
CN103908365A (en) * | 2014-04-09 | 2014-07-09 | 天津思博科科技发展有限公司 | Electronic travel assisting device |
-
2016
- 2016-10-09 CN CN201610881571.XA patent/CN106408932B/en active Active
Patent Citations (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080309468A1 (en) * | 2007-06-12 | 2008-12-18 | Greene Daniel H | Human-machine-interface (HMI) customization based on collision assessments |
CN101853399A (en) * | 2010-05-11 | 2010-10-06 | 北京航空航天大学 | Method for realizing blind road and pedestrian crossing real-time detection by utilizing computer vision technology |
CN101964054A (en) * | 2010-09-29 | 2011-02-02 | 东南大学 | Friendly track detection system based on visual processing |
CN102609724A (en) * | 2012-02-16 | 2012-07-25 | 北京航空航天大学 | Method for prompting ambient environment information by using two cameras |
CN103908365A (en) * | 2014-04-09 | 2014-07-09 | 天津思博科科技发展有限公司 | Electronic travel assisting device |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108960029A (en) * | 2018-03-23 | 2018-12-07 | 北京交通大学 | A kind of pedestrian diverts one's attention behavioral value method |
CN108524209A (en) * | 2018-03-30 | 2018-09-14 | 江西科技师范大学 | Blind-guiding method, system, readable storage medium storing program for executing and mobile terminal |
CN109241916A (en) * | 2018-09-12 | 2019-01-18 | 四川长虹电器股份有限公司 | A kind of system and method for pedestrian's walking safety detection based on smart phone |
Also Published As
Publication number | Publication date |
---|---|
CN106408932B (en) | 2018-11-16 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN112069944B (en) | Road congestion level determining method | |
CN104933870B (en) | Vehicle fake plate identification method and device based on vehicle behavior analysis | |
CN109190488B (en) | Front vehicle door opening detection method and device based on deep learning YOLOv3 algorithm | |
CN111563494A (en) | Behavior identification method and device based on target detection and computer equipment | |
CN108509954A (en) | A kind of more car plate dynamic identifying methods of real-time traffic scene | |
CN107170247B (en) | Method and device for determining queuing length of intersection | |
CN109495654B (en) | Pedestrian safety sensing method based on smart phone | |
KR102015947B1 (en) | Method for extracting image of learning object for autonomous driving and apparatus thereof | |
CN111460924B (en) | Gate ticket-evading behavior detection method based on target detection | |
CN105539450B (en) | A kind of automatic identifying method and device driving stroke | |
CN103324955A (en) | Pedestrian detection method based on video processing | |
CN110866479A (en) | Method, device and system for detecting that motorcycle driver does not wear helmet | |
CN104123532B (en) | Target object detection and target object quantity confirming method and device | |
CN103909826A (en) | Optimization method for collaboratively sensing violation behavior of drivers | |
CN106408932A (en) | Mobile terminal based pre-warning system for distractive walks | |
CN104112279B (en) | A kind of object detection method and device | |
CN110322687B (en) | Method and device for determining running state information of target intersection | |
CN103345842A (en) | Road vehicle classification system and method | |
CN106682600A (en) | Method and terminal for detecting targets | |
CN110674887A (en) | End-to-end road congestion detection algorithm based on video classification | |
CN103324957A (en) | Identification method and identification device of state of signal lamps | |
CN114494998B (en) | Intelligent analysis method and system for vehicle data | |
CN115223124A (en) | Method and device for fitting lane line equation, vehicle and storage medium | |
CN113887431A (en) | AI-based detection method for identifying person without gloves in kitchen scene | |
TW201822168A (en) | Vehicle moving direction predicting system and method using digital image recognition in combination with moving trace computation technology with application of big data computation technology |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |