CN107886523A - Vehicle target movement velocity detection method based on unmanned plane multi-source image - Google Patents
Vehicle target movement velocity detection method based on unmanned plane multi-source image Download PDFInfo
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
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Abstract
The present invention discloses a kind of vehicle target movement velocity detection method based on unmanned plane multi-source image, comprises the following steps：Build unmanned plane multi-source image acquisition platform；Image acquisitions are carried out using image collection platform, the multi-source image of Visible Light Camera and thermal infrared camera is obtained by the computer main board of unmanned aerial vehicle platform, multi-source image data are transmitted to ground monitoring client by 4G, complete the real-time acquisition of image；The image data of acquisition is corrected, to reduce the geometric distortion of image data；Registration is carried out to the multi-source image data after correction；Fusion is weighted to the polynary image data after registration；Based on the multi-source image data after Weighted Fusion, vehicle target is detected；Vehicle target is tracked；Calculate vehicle target movement velocity.The present invention provides a kind of easy to operate, high efficiency, the speed monitor mode of maneuverability for vehicle monitoring management department.
Description
Technical field
The present invention relates to technical field of video image processing, more particularly to a kind of vehicle mesh based on unmanned plane multi-source image
Mark movement velocity detection method.
Background technology
With the continuous development of scientific technology, Modern Traffic is more and more important in economic activity, and along with traffic
Modernization development, there are increasing traffic problems, under such overall situation, intelligent transportation system（Intelligent
Traffic System, ITS）Arise at the historic moment, various countries researcher is actively researched and developed to improve traffic monitoring method, wherein calculating
Machine vision technique provides important technology for intelligent transportation system and supported.The detection of Velicle motion velocity is intelligent transportation system
Focus and difficult point in important step, and research, monitoring to car speed on the one hand can monitor overspeed violation problem with
And judge road congested conditions, on the other hand vehicle interested can be monitored, and then rapid progress of taking measures on customs clearance
The maintenance of road traffic, so as to realize intelligent transportation system.
Vehicle speed detection is the basis of intelligent traffic monitoring system, and current most vehicle speed detection relies on electronics
Camera system carries out traffic control, and then realizes the purpose of unmanned monitoring, and conventional method has both at home and abroad：Magnetic tests the speed, radar
Test the speed, it is infrared test the speed, laser velocimeter and video frequency speed-measuring etc..Wherein the detection technique of electromagnetic induction principle is comparatively multiple because arranging
Miscellaneous, monitoring place is relatively more fixed；Laser and radar detection technique relative cost are higher, certain error be present so that actually should
It is difficult to promote in.This means current car speed monitoring there is monitoring place flexibility it is poor, have one to illumination condition
Fixed dependence, it is more difficult to which test problems are carried out to single vehicle interested.
And with unmanned plane（Unmanned Aerial Vehicle）Fast development, its manipulation is convenient, can carry more
Business equipment, and complete a variety of generic tasks.Along with high-altitude power technology, accurate landing technology, the development of the communication technology so that nothing
Man-machine performance is gradually perfect, and function increasingly extends, and is more widely applied, and it is indispensable to occupy this in terms of civilian resource investigation
Status.Good data acquisition platform is provided this means unmanned plane detects for vehicle target.And along with thermal infrared camera
Continuous development, it possesses good imaging capability, and the wavelength of thermal infrared exceedes visible ray, it is possible to provide it is more various
Data, in addition, the cost of thermal infrared camera is also declining.
The content of the invention
It is an object of the present invention to provide a kind of vehicle target movement velocity detection method based on unmanned plane multi-source image, the base
Overcome defect present in prior art, increase vehicle speed in the vehicle target movement velocity detection method of unmanned plane multi-source image
The flexibility of detection is spent, the application of detection method has been expanded, has reduced the dependence to illumination condition.
To reach above-mentioned purpose, the technical solution adopted by the present invention is：A kind of vehicle mesh based on unmanned plane multi-source image
Mark movement velocity detection method, it is characterised in that：Comprise the following steps：
Step 1: build unmanned plane multi-source image acquisition platform；
Step 2: carry out image acquisitions using image collection platform；
Step 3: the image data of acquisition is corrected, to reduce the geometric distortion of image data；
Step 4: registration is carried out to the multi-source image data after correction；
Step 5: fusion is weighted to the polynary image data after registration；
Step 6: based on the multi-source image data after Weighted Fusion, vehicle target is detected；
Step 7: vehicle target is tracked；
Step 8: calculate vehicle target movement velocity.
Further improved scheme is as follows in above-mentioned technical proposal：
1. in such scheme, the unmanned plane multi-source image acquisition platform is gone forward side by side to carry Visible Light Camera and thermal infrared camera
The unmanned aerial vehicle platform of row multi-source image collection.
It is described Step 3: visible image to acquisition, uses traditional gridiron pattern to carry out geometry 2. in such scheme
Correction.
3. in such scheme, it is described Step 4: using homography matrix by after geometric correction can be by optical image and thermal infrared
Image carries out registration.
It is 4. described Step 6: using the multi-source image information after Weighted Fusion, to improve vehicle target in such scheme
The precision of detection and the position of optimization aim frame.
5. in such scheme, use YOLO（You Only Look Once）Deep learning detects to vehicle target.
6. carrying out detection to vehicle target in such scheme, in the step 6 specifically includes following sub-step：
Sub-step S61, the multi-source image after Weighted Fusion cut；
Sub-step S62, mark multi-source image in vehicle target, and by the vehicle target marked be divided into training dataset and
Test data set；
Sub-step S63, training parameter is set, wherein mainly including：Criticize size（batch size）, weights decay and learning rate；
Sub-step S64, the training dataset marked off is trained, obtains convergent training pattern；
Sub-step S65, application training model detect to vehicle target one by one, and testing result preserves.
7. in such scheme, being tracked in the step 7 to vehicle target, i.e., examined according to vehicle target in step 6
Result is surveyed, matching and the tracking prediction of adjacent video interframe are carried out to vehicle, specifically includes following sub-step：
Sub-step S71, to each pair consecutive image, SURF corresponding to one group of extraction（Speeded Up Robust Features）It is special
Sign point；
Sub-step S72, relative transform matrix calculated according to characteristic point, estimate the relative attitude of active view, i.e., relative to upper one
The position of view；
Sub-step S73, using Kalman Filtering for Discrete device the vehicle identified is tracked and predicted, according to sub-step S72
In relative transform matrix correct vehicle location, calculate and prepare for follow-up vehicle movement distance.
It is 8. described Step 8: calculating vehicle target movement velocity, i.e., according to known target length computation in such scheme
Image resolution ratio, and moved according to the pixel of vehicle tracking result calculating target vehicle, and then vehicle operating range is calculated, pass through
The frequency meter for gathering image calculates the time interval of IMAQ, finally calculates speed according to speed calculation formula.
Because above-mentioned technical proposal is used, the present invention has following advantages compared with prior art：
Vehicle target movement velocity detection method of the invention based on unmanned plane multi-source image, it is put down based on flexible unmanned plane
Platform, realize the speed detecting/monitoring to road vehicle or vehicle target interested；Road traffic condition can not only flexibly be obtained
Multi-source image, and the increase of thermal infrared imagery can cause this method to be equally applicable to not possess illumination condition situation, be vehicle
Monitoring management department provides a kind of easy to operate, high efficiency, the speed monitor mode of maneuverability.
Brief description of the drawings
Accompanying drawing 1 is the flow chart of vehicle target movement velocity detection method of the present invention.
Embodiment
Below in conjunction with the accompanying drawings and embodiment the invention will be further described：
Embodiment：A kind of vehicle target movement velocity detection method based on unmanned plane multi-source image, it is characterised in that：Including with
Lower step：
Step 1: build unmanned plane multi-source image acquisition platform；
Step 2: carrying out image acquisitions using image collection platform, obtained by the computer main board of unmanned aerial vehicle platform visible
The multi-source image of light camera and thermal infrared camera, multi-source image data are transmitted to ground monitoring client by 4G, complete shadow
The real-time acquisition of picture；
Step 3: the image data of acquisition is corrected, to reduce the geometric distortion of image data；
Step 4: registration is carried out to the multi-source image data after correction；
Step 5: fusion is weighted to the polynary image data after registration；
Step 6: based on the multi-source image data after Weighted Fusion, vehicle target is detected；
Step 7: vehicle target is tracked；
Step 8: calculate vehicle target movement velocity.
Above-mentioned unmanned plane multi-source image acquisition platform is carrying Visible Light Camera and thermal infrared camera and carries out multi-source image
The unmanned aerial vehicle platform of collection.
Above-mentioned steps three, the visible image to acquisition, geometric correction is carried out using traditional gridiron pattern.
Above-mentioned steps four, using homography matrix will be after geometric correction optical image and thermal infrared imagery can be subjected to registration.
Above-mentioned steps six, using the multi-source image information after Weighted Fusion, to improve the precision of vehicle target detection and excellent
Change the position of target frame.
Use YOLO（You Only Look Once）Deep learning detects to vehicle target.
Detection is carried out to vehicle target in above-mentioned steps six and specifically includes following sub-step：
Sub-step S61, the multi-source image after Weighted Fusion cut so that the regional extent of image is consistent；
Sub-step S62, mark multi-source image in vehicle target, and by the vehicle target marked be divided into training dataset and
Test data set；
Sub-step S63, the training parameter that deep learning is set, wherein mainly including：Criticize size（batch size）, weights decay
And learning rate；
Sub-step S64, the training dataset marked off is trained, obtains convergent training pattern；
Sub-step S65, application training model detect to vehicle target one by one, and testing result preserves.
Vehicle target is tracked in above-mentioned steps seven, i.e., according to vehicle target testing result in step 6, to vehicle
Matching and the tracking prediction of adjacent video interframe are carried out, specifically includes following sub-step：
Sub-step S71, to each pair consecutive image, SURF corresponding to one group of extraction（Speeded Up Robust Features）It is special
Sign point；
Sub-step S72, relative transform matrix calculated according to characteristic point, estimate the relative attitude of active view, i.e., relative to upper one
The position of view；
Sub-step S73, using Kalman Filtering for Discrete device the vehicle identified is tracked and predicted, according to sub-step S72
In relative transform matrix correct vehicle location, calculate and prepare for follow-up vehicle movement distance.
Above-mentioned steps eight, vehicle target movement velocity is calculated, i.e. foundation known target length computation image resolution ratio, and root
The pixel that target vehicle is calculated according to vehicle tracking result moves, and then calculates vehicle operating range, by the frequency for gathering image
The time interval of IMAQ is calculated, speed is finally calculated according to speed calculation formula.
This implementation is explained further as follows：
Unmanned plane multi-source data acquiring system proposed by the present invention, including：Unmanned aerial vehicle platform, power supply, computer main board, ground monitoring
Client, Visible Light Camera, thermal infrared camera, camera fixed mount, image pick-up card, 4G modules and base station, the unmanned plane are put down
Platform is equipped with flight controller and possesses dynamical system, GPS and battery etc., and support module is expanded, the computer main board, visible
Light camera and thermal infrared camera are both secured on unmanned aerial vehicle platform, and described image capture card is used to ensure that computer main board acquisition heat is red
The image data of outer camera, the computer main board are provided with image pick-up card driving, are developed using the supporting SDK of image pick-up card
Structure, programming synchronous obtain can be equipped on the gathered data of light camera and thermal infrared camera, the 4G modules on computer main board
And base station is connected by auto dialing, the ground monitoring client is connected to base station, ensures to be mounted in the computer master of unmanned plane
Plate is connected with ground monitoring client.
In specific implementation, the output voltage of power supply is 12V, output current 1A；The Visible Light Camera is USB connections
Industrial camera, pixel size are 5.2 μm, rated voltage 12V, rated current 80mA of 5.0 μ m；The thermal infrared camera
Wave-length coverage is 8~14 μm, and resolution ratio is 640 × 480（Pixel）, rated voltage 12V；Camera fixed mount uses 3D printer
Printing.
In specific implementation, computer main board is encoded using quick jpeg compressed encodings storehouse JPEG-turbo, to the more of acquisition
Source image is compressed coding, carries out image transmitting using boost asio TCP, the image transmitting after compression is supervised to ground
Client is controlled, boost asio TCP corresponding to the use of ground monitoring client receive compression image, and use JPEG-turbo
Decoding display.
During using vehicle target movement velocity detection method based on unmanned plane multi-source image, can be achieved to road vehicle or
The speed detecting/monitoring of person's vehicle target interested, the multi-source image of road traffic condition can be flexibly obtained, and suitable for not possessing
The situation of illumination condition, a kind of easy to operate, high efficiency, the speed monitoring side of maneuverability are provided for vehicle monitoring management department
Formula.
The above embodiments merely illustrate the technical concept and features of the present invention, and its object is to allow person skilled in the art
Scholar can understand present disclosure and implement according to this, and it is not intended to limit the scope of the present invention.It is all according to the present invention
The equivalent change or modification that Spirit Essence is made, it should all be included within the scope of the present invention.
Claims (9)
- A kind of 1. vehicle target movement velocity detection method based on unmanned plane multi-source image, it is characterised in that：Including following step Suddenly：Step 1: build unmanned plane multi-source image acquisition platform；Step 2: carry out image acquisitions using image collection platform；Step 3: the image data of acquisition is corrected, to reduce the geometric distortion of image data；Step 4: registration is carried out to the multi-source image data after correction；Step 5: fusion is weighted to the polynary image data after registration；Step 6: based on the multi-source image data after Weighted Fusion, vehicle target is detected；Step 7: vehicle target is tracked；Step 8: calculate vehicle target movement velocity.
- 2. the vehicle target movement velocity detection method according to claim 1 based on unmanned plane multi-source image, its feature It is：The unmanned plane multi-source image acquisition platform is carrying Visible Light Camera and thermal infrared camera and carries out multi-source image collection Unmanned aerial vehicle platform.
- 3. the vehicle target movement velocity detection method according to claim 1 based on unmanned plane multi-source image, its feature It is：It is described Step 3: visible image to acquisition, uses traditional gridiron pattern to carry out geometric correction.
- 4. the vehicle target movement velocity detection method according to claim 1 based on unmanned plane multi-source image, its feature It is：It is described Step 4: using homography matrix will be after geometric correction optical image and thermal infrared imagery can be subjected to registration.
- 5. the vehicle target movement velocity detection method according to claim 1 based on unmanned plane multi-source image, its feature It is：It is described Step 6: using the multi-source image information after Weighted Fusion, to improve the precision of vehicle target detection and optimize mesh Mark the position of frame.
- 6. the vehicle target movement velocity detection method according to claim 1 based on unmanned plane multi-source image, its feature It is：Use YOLO（You Only Look Once）Deep learning detects to vehicle target.
- 7. the vehicle target movement velocity detection method according to claim 6 based on unmanned plane multi-source image, its feature It is：Detection is carried out to vehicle target in the step 6 and specifically includes following sub-step：Sub-step S61, the multi-source image after Weighted Fusion cut；Sub-step S62, mark multi-source image in vehicle target, and by the vehicle target marked be divided into training dataset and Test data set；Sub-step S63, training parameter is set, wherein mainly including：Criticize size（batch size）, weights decay and learning rate；Sub-step S64, the training dataset marked off is trained, obtains convergent training pattern；Sub-step S65, application training model detect to vehicle target one by one, and testing result preserves.
- 8. the vehicle target movement velocity detection method according to claim 1 based on unmanned plane multi-source image, its feature It is：Vehicle target is tracked in the step 7, i.e., according to vehicle target testing result in step 6, vehicle carried out The matching of adjacent video interframe and tracking prediction, specifically include following sub-step：Sub-step S71, to each pair consecutive image, SURF corresponding to one group of extraction（Speeded Up Robust Features）It is special Sign point；Sub-step S72, relative transform matrix calculated according to characteristic point, estimate the relative attitude of active view, i.e., relative to upper one The position of view；Sub-step S73, using Kalman Filtering for Discrete device the vehicle identified is tracked and predicted, according to sub-step S72 In relative transform matrix correct vehicle location, calculate and prepare for follow-up vehicle movement distance.
- 9. the vehicle target movement velocity detection method according to claim 1 based on unmanned plane multi-source image, its feature It is：It is described Step 8: calculate vehicle target movement velocity, i.e., foundation known target length computation image resolution ratio, and according to Vehicle tracking result calculates the pixel movement of target vehicle, and then calculates vehicle operating range, by the frequency meter for gathering image The time interval of IMAQ is calculated, speed is finally calculated according to speed calculation formula.
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Cited By (9)
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| CN108537278A (en) * | 2018-04-10 | 2018-09-14 | 中国人民解放军火箭军工程大学 | A kind of Multi-source Information Fusion single goal location determining method and system |
| CN108875600A (en) * | 2018-05-31 | 2018-11-23 | 银江股份有限公司 | A kind of information of vehicles detection and tracking method, apparatus and computer storage medium based on YOLO |
| CN109523528A (en) * | 2018-11-12 | 2019-03-26 | 西安交通大学 | A kind of transmission line of electricity extracting method based on unmanned plane binocular vision SGC algorithm |
| CN109919058A (en) * | 2019-02-26 | 2019-06-21 | 武汉大学 | A kind of multisource video image highest priority rapid detection method based on Yolo V3 |
| CN109934848A (en) * | 2019-03-07 | 2019-06-25 | 贵州大学 | A method of the moving object precise positioning based on deep learning |
| CN111009012A (en) * | 2019-11-29 | 2020-04-14 | 四川沃洛佳科技有限公司 | Unmanned aerial vehicle speed measurement method based on computer vision, storage medium and terminal |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108537278A (en) * | 2018-04-10 | 2018-09-14 | 中国人民解放军火箭军工程大学 | A kind of Multi-source Information Fusion single goal location determining method and system |
| CN108875600A (en) * | 2018-05-31 | 2018-11-23 | 银江股份有限公司 | A kind of information of vehicles detection and tracking method, apparatus and computer storage medium based on YOLO |
| CN109523528B (en) * | 2018-11-12 | 2021-07-13 | 西安交通大学 | Power transmission line extraction method based on unmanned aerial vehicle binocular vision SGC algorithm |
| CN109523528A (en) * | 2018-11-12 | 2019-03-26 | 西安交通大学 | A kind of transmission line of electricity extracting method based on unmanned plane binocular vision SGC algorithm |
| CN109919058A (en) * | 2019-02-26 | 2019-06-21 | 武汉大学 | A kind of multisource video image highest priority rapid detection method based on Yolo V3 |
| CN109934848A (en) * | 2019-03-07 | 2019-06-25 | 贵州大学 | A method of the moving object precise positioning based on deep learning |
| CN109934848B (en) * | 2019-03-07 | 2023-05-23 | 贵州大学 | Method for accurately positioning moving object based on deep learning |
| CN111009012A (en) * | 2019-11-29 | 2020-04-14 | 四川沃洛佳科技有限公司 | Unmanned aerial vehicle speed measurement method based on computer vision, storage medium and terminal |
| CN111009012B (en) * | 2019-11-29 | 2023-07-28 | 四川沃洛佳科技有限公司 | Unmanned aerial vehicle speed measuring method based on computer vision, storage medium and terminal |
| CN112215070A (en) * | 2020-09-10 | 2021-01-12 | 佛山聚卓科技有限公司 | Unmanned aerial vehicle aerial photography video traffic flow statistical method, host and system |
| CN112364561A (en) * | 2020-10-26 | 2021-02-12 | 上海感探号信息科技有限公司 | Vehicle control action correction method and device, electronic equipment and storage medium |
| CN112699854A (en) * | 2021-03-22 | 2021-04-23 | 亮风台（上海）信息科技有限公司 | Method and device for identifying stopped vehicle |
| CN112699854B (en) * | 2021-03-22 | 2021-07-20 | 亮风台（上海）信息科技有限公司 | Method and device for identifying stopped vehicle |
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