CN103456171A - Vehicle flow detection system and method based on fish-eye lens and image correction method - Google Patents
Vehicle flow detection system and method based on fish-eye lens and image correction method Download PDFInfo
- Publication number
- CN103456171A CN103456171A CN2013103950957A CN201310395095A CN103456171A CN 103456171 A CN103456171 A CN 103456171A CN 2013103950957 A CN2013103950957 A CN 2013103950957A CN 201310395095 A CN201310395095 A CN 201310395095A CN 103456171 A CN103456171 A CN 103456171A
- Authority
- CN
- China
- Prior art keywords
- fish
- image
- vehicle
- eye
- formula
- 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
Images
Landscapes
- Image Analysis (AREA)
- Image Processing (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a vehicle flow detection system and method based on a fish-eye lens and an image correction method. The vehicle flow detection system comprises an image collection device, a vehicle flow detection module and a data storage device. The vehicle detection module is respectively connected with the image collection device and the data storage device; the image collection device comprises a fish-eye lens and a sensor which are integrated, and the sensor is connected with the vehicle flow detection module through an image correction chip. The vehicle flow detection system based on the fish-eye lens and the image correction method solve the problems that an existing vehicle flow monitoring system is not abundant enough in monitoring scene and high in engineering manufacturing cost.
Description
Technical field
The invention belongs to a kind of vehicle flow detection system, be specifically related to based on fish-eye vehicle flow detection system, method and method for correcting image.
Background technology
Existing monitoring system adopts single common view angle camera lens, and vehicle flowrate is monitored; Adopt the system of common lens, the scene of monitoring is abundant not, and the current flux monitoring system mainly operates on industrial computer, has so not only increased the construction costs cost, and has needed larger industrial computer equipment to be processed.
Summary of the invention
Abundant not in order to solve the scene that has the monitoring of vehicle flowrate monitoring system now, and construction costs high in cost of production problem, the present invention has designed a kind of based on fish-eye vehicle flow detection system and method for correcting image.
A kind of based on fish-eye vehicle flow detection system, comprise image collecting device, vehicle Flow Detection module and data storage device, the vehicle Flow Detection module connects respectively image collecting device and data storage device, image collecting device comprises integrated fish eye lens and sensor, and sensor connects the vehicle Flow Detection module by the image rectification chip.Its advantage is: system of the present invention comprises that three parts form, first's image collecting device, and this part mainly is comprised of fish eye lens and sensor imaging moiety; Second portion vehicle Flow Detection module realizes vehicle flowrate is checked in this part; The third part data memory device, the data after this part is processed second portion are stored.Having fish-eye system has the vehicle flowrate on monitoring road surface, better visual angle; The scope checked is wider, and native system does not need additional checkout facility, has reduced cost.
Described based on fish-eye vehicle flow detection system, the image rectification chip is fpga chip, and the vehicle Flow Detection module is the DSP data processing module.Its advantage is: the fish eye lens collected by camera, after view data, is processed through correction algorithm, and the image restoring of distortion is become to normal picture, sends to the vehicle Flow Detection module.Fish eye lens is proofreaied and correct and is processed in fpga chip, need to open up in fpga chip inside a memory cache district, and this zone is used for storing the view data after proofreading and correct.At first, calculate the position of the coordinate points of two field picture after correcting and the corresponding relation of the coordinate points on former two field picture, then this on former two field picture put locational numerical value assignment to the rectification of buffer zone inside after on the corresponding point of frame, then dispose this frame internal storage data and carry out the next frame processing.The image that data processing module receives after video acquisition module is corrected carries out the vehicle flowrate analysis.A panel region in this module intercepts image is analyzed, and vehicle sails this zone into and is not more than the length of general vehicle in image with the distance of leaving this zone, and this zone need to be demarcated according to actual scene.
A kind of based on fish-eye traffic flow detecting method, comprise the following steps:
Step 1, the fish eye lens acquisition of image data, and be sent to FPGA image rectification chip by sensor;
Step 2, FPGA image rectification chip is proofreaied and correct fault image;
Step 3, the yuv data image binaryzation after the self-adaption binaryzation module in the DSP data processing module will be proofreaied and correct is processed;
Step 4, the DSP data processing module adopts gradient vehicle local feature to detect and whether HOG vehicle characteristics matching process joint-detection has vehicle to pass through;
Step 5, the image information that will contain vehicle deposits data storage device in.
Described based on fish-eye traffic flow detecting method, the gray-scale value in step 3 in self-adaption binaryzation module image is set to 0 or 1, and the setting of threshold value adopts the method for adaptive threshold.
Described based on fish-eye traffic flow detecting method, in step 4, gradient information is processed the pattern that adopts one dimension or two dimension.
Described based on fish-eye traffic flow detecting method, HOG vehicle characteristics matching process in step 4, the HOG feature of employing headstock or tailstock model, carry out off-line training and classification with SVM support vector machine linear classifier.
Its advantage is: the fish eye lens collected by camera, after view data, is proofreaied and correct correcting algorithm through fisheye image and is processed, and the image restoring of distortion is become to normal picture, sends to the vehicle Flow Detection module.Yuv data image binaryzation after the self-adaption binaryzation module will be proofreaied and correct is processed.Gray-scale value in image is set to 0 or 1, and the setting of threshold value adopts the method for adaptive threshold.Whether the detection of gradient vehicle local feature and HOG vehicle characteristics matching process join together to detect surveyed area in this two field picture has vehicle to pass through.There is abundant gradient information the vehicle part, the information of vehicles in the analyzing and testing zone that employing the method can be rough.Gradient information is processed the pattern that can adopt one dimension or two dimension, and the higher need of the dimension time to be processed is longer.HOG vehicle characteristics matching process adopts the HOG feature of headstock or tailstock model, with SVM support vector machine linear classifier, carries out off-line training and classification.Utilize this feature can judge which image pattern contains vehicle, those image patterns do not have vehicle, thereby carry out accurate vehicle flowrate.The image data information that will contain vehicle is filed, and returns and starts to carry out data analysis next time.
A kind of fish-eye method for correcting image comprises the following steps:
(1) according to the spherical co-ordinate model in the panorama picture of fisheye lens principle, set up the XYZ space rectangular coordinate system, wherein, P is for inciding fish-eye light; F1 is fish-eye focal length; The angle of light P and Z axis is θ; A(x1, y1) for light P falls the point on the plane that x axle y axle forms through the common lens perfect condition, with Z axis, form angle β, form angle ψ with X-axis, A point and central point apart from being R; K is the projective invariant coefficient; B(x2, y2) for the point of P on the plane of falling x axle y axle after fish eye lens refraction and forming, with Z axis, form angle α, with the angle of X-axis be ψ, B point and central point apart from being r;
(2) by the conversion relation of β and θ and θ and α, determine α and β angle; The conversion relation formula of β and θ is:
The rectangular projection formula of fish eye lens in the limited range of plane is as follows:
By formula, 1. with formula, 2. obtained
α and β angle are
⑤
(3) 3., 4. and 5. obtain the relation of R and r by the formula in step (2):
(4) convert the B point to the polar coordinates formula from planimetric coordinates:
⑦
(5) determine A(x1, the y1 that light P obtains under conventional lens shooting) coordinate:
(6) 6. with formula, 7. obtain the range formula of R by formula:
(7) according to formula 8., 9., 10.,, obtain the coordinate transformation relation that A point and B are ordered:
Utilize this relation to become normal picture to the image restoring of the distortion of fisheye camera shooting preferably.Then normal picture after conversion is delivered to the DSP data processing module and is carried out vehicle flowrate calculating.The fish eye lens correction algorithm is exactly the pixel and the coordinate points corresponding relation of correcting on rear image solved on former frame.
The accompanying drawing explanation
Fig. 1 is fish-eye vehicle flow detection system principle;
Fig. 2 is fish-eye vehicle flow detection system processing procedure;
Fig. 3 is the correction handling principle figure of flake video in FPGA inside;
Fig. 4 is video mode vehicle Flow Detection schematic diagram;
Fig. 5 is the vehicle flow detection algorithm process flow diagram;
Fig. 6 is fish eye lens correcting algorithm three-dimensional imaging figure;
Fig. 7 is fish eye lens correcting algorithm planar imaging figure.
Embodiment
Below in conjunction with accompanying drawing, structure of the present invention is explained in detail to explanation, as Fig. 1 is fish-eye vehicle flow detection system principle; A kind of based on fish-eye vehicle flow detection system, comprise image collecting device, vehicle Flow Detection module and data storage device, the vehicle Flow Detection module connects respectively image collecting device and data storage device, image collecting device comprises integrated fish eye lens and sensor, and sensor connects the vehicle Flow Detection module by the image rectification chip.As Fig. 2 is fish-eye vehicle flow detection system processing procedure; Described based on fish-eye vehicle flow detection system, the image rectification chip is fpga chip, and the vehicle Flow Detection module is the DSP data processing module.
A kind of based on fish-eye traffic flow detecting method, comprise the following steps:
Step 1, the fish eye lens acquisition of image data, and be sent to FPGA image rectification chip by sensor;
Step 2, FPGA image rectification chip is proofreaied and correct fault image;
Step 3, the yuv data image binaryzation after the self-adaption binaryzation module in the DSP data processing module will be proofreaied and correct is processed;
Step 4, the DSP data processing module adopts gradient vehicle local feature to detect and whether HOG vehicle characteristics matching process joint-detection has vehicle to pass through;
Step 5, the image information that will contain vehicle deposits data storage device in.
Described based on fish-eye traffic flow detecting method, the gray-scale value in step 3 in self-adaption binaryzation module image is set to 0 or 1, and the setting of threshold value adopts the method for adaptive threshold.
Described based on fish-eye traffic flow detecting method, in step 4, gradient information is processed the pattern that adopts one dimension or two dimension.
Described based on fish-eye traffic flow detecting method, HOG vehicle characteristics matching process in step 4, the HOG feature of employing headstock or tailstock model, carry out off-line training and classification with SVM support vector machine linear classifier.
A kind of fish-eye method for correcting image comprises the following steps:
(1) according to the spherical co-ordinate model in the panorama picture of fisheye lens principle, set up the XYZ space rectangular coordinate system, wherein, P is for inciding fish-eye light; F1 is fish-eye focal length; The angle of light P and Z axis is θ; A(x1, y1) for light P falls the point on the plane that x axle y axle forms through the common lens perfect condition, with Z axis, form angle β, form angle ψ with X-axis, A point and central point apart from being R; K is the projective invariant coefficient; B(x2, y2) for the point of P on the plane of falling x axle y axle after fish eye lens refraction and forming, with Z axis, form angle α, with the angle of X-axis be ψ, B point and central point apart from being r;
(2) by the conversion relation of β and θ and θ and α, determine α and β angle; The conversion relation formula of β and θ is:
The rectangular projection formula of fish eye lens in the limited range of plane is as follows:
By formula, 1. with formula, 2. obtained
α and β angle are
⑤
(3) 3., 4. and 5. obtain the relation of R and r by the formula in step (2): as Fig. 7 is fish eye lens correcting algorithm planar imaging figure;
(4) convert the B point to the polar coordinates formula from planimetric coordinates:
(5) determine A(x1, the y1 that light P obtains under conventional lens shooting) coordinate:
(6) 6. with formula, 7. obtain the range formula of R by formula:
(7) according to formula 8., 9., 10.,, obtain the coordinate transformation relation that A point and B are ordered:
The fish eye lens collected by camera is after view data, at first, calculate the position of the coordinate points of two field picture after correcting and the corresponding relation of the coordinate points on former two field picture, then this on former two field picture put locational numerical value assignment to the rectification of buffer zone inside after on the corresponding point of frame, then dispose this frame internal storage data and carry out the next frame processing.The fish eye lens correction algorithm is exactly the pixel and the coordinate points corresponding relation of correcting on rear image solved on former frame, and correcting principle figure as shown in Figure 3; Proofread and correct correcting algorithm through fisheye image and process, the image restoring of distortion is become to normal picture, send to the vehicle Flow Detection module.The image that data processing module receives after video acquisition module is corrected carries out the vehicle flowrate analysis.A panel region in this module intercepts image is analyzed, as Fig. 4 is video mode vehicle Flow Detection schematic diagram; Vehicle sails this zone into and leaves this regional distance H and is not more than the length of general vehicle in image, and this zone need to be demarcated according to actual scene.Yuv data image binaryzation after the self-adaption binaryzation module will be proofreaied and correct is processed.Gray-scale value in image is set to 0 or 1, and the setting of threshold value adopts the method for adaptive threshold.Whether the detection of gradient vehicle local feature and HOG vehicle characteristics matching process join together to detect surveyed area in this two field picture has vehicle to pass through.There is abundant gradient information the vehicle part, the information of vehicles in the analyzing and testing zone that employing the method can be rough.Gradient information is processed the pattern that can adopt one dimension or two dimension, and the higher need of the dimension time to be processed is longer.HOG vehicle characteristics matching process adopts the HOG feature of headstock or tailstock model, with SVM support vector machine linear classifier, carries out off-line training and classification.Utilize this feature can judge which image pattern contains vehicle, those image patterns do not have vehicle, thereby carry out accurate vehicle flowrate.The image data information that will contain vehicle is filed, and returns and starts to carry out data analysis next time.As Fig. 5 is the vehicle flow detection algorithm process flow diagram.
Technique scheme has only embodied the optimal technical scheme of technical solution of the present invention, and those skilled in the art have all embodied principle of the present invention to some changes that wherein some part may be made, within belonging to protection scope of the present invention.
Claims (7)
1. one kind based on fish-eye vehicle flow detection system, comprise image collecting device, vehicle Flow Detection module and data storage device, the vehicle Flow Detection module connects respectively image collecting device and data storage device, it is characterized in that, image collecting device comprises integrated fish eye lens and sensor, and sensor connects the vehicle Flow Detection module by the image rectification chip.
2. according to claim 1ly based on fish-eye vehicle flow detection system, it is characterized in that, the image rectification chip is fpga chip, and the vehicle Flow Detection module is the DSP data processing module.
According to claim 2 based on fish-eye traffic flow detecting method, it is characterized in that, comprise the following steps:
Step 1, the fish eye lens acquisition of image data, and be sent to FPGA image rectification chip by sensor;
Step 2, FPGA image rectification chip is proofreaied and correct fault image;
Step 3, the yuv data image binaryzation after the self-adaption binaryzation module in the DSP data processing module will be proofreaied and correct is processed;
Step 4, the DSP data processing module adopts gradient vehicle local feature to detect and whether HOG vehicle characteristics matching process joint-detection has vehicle to pass through;
Step 5, the image information that will contain vehicle deposits data storage device in.
4. according to claim 3ly based on fish-eye traffic flow detecting method, it is characterized in that, the gray-scale value in step 3 in self-adaption binaryzation module image is set to 0 or 1, and the setting of threshold value adopts the method for adaptive threshold.
5. according to claim 3ly based on fish-eye traffic flow detecting method, it is characterized in that, in step 4, gradient information is processed the pattern that adopts one dimension or two dimension.
6. according to claim 3 based on fish-eye traffic flow detecting method, it is characterized in that, HOG vehicle characteristics matching process in step 4, the HOG feature of employing headstock or tailstock model, carry out off-line training and classification with SVM support vector machine linear classifier.
7. a fish-eye method for correcting image, is characterized in that, comprises the following steps:
(1) according to the spherical co-ordinate model in the panorama picture of fisheye lens principle, set up the XYZ space rectangular coordinate system, wherein, P is for inciding fish-eye light; F1 is fish-eye focal length; The angle of light P and Z axis is θ; A(x1, y1) for light P falls the point on the plane that x axle y axle forms through the common lens perfect condition, with Z axis, form angle β, form angle ψ with X-axis, A point and central point apart from being R; K is the projective invariant coefficient; B(x2, y2) for the point of P on the plane of falling x axle y axle after fish eye lens refraction and forming, with Z axis, form angle α, with the angle of X-axis be ψ, B point and central point apart from being r;
(2) by the conversion relation of β and θ and θ and α, determine α and β angle; The conversion relation formula of β and θ is:
The rectangular projection formula of fish eye lens in the limited range of plane is as follows:
By formula, 1. with formula, 2. obtained
α and β angle are
⑤
(3) 3., 4. and 5. obtain the relation of R and r by the formula in step (2):
(4) convert the B point to the polar coordinates formula from planimetric coordinates:
(5) determine A(x1, the y1 that light P obtains under conventional lens shooting) coordinate:
(6) 6. with formula, 7. obtain the range formula of R by formula:
(7) according to formula 8., 9., 10.,, obtain the coordinate transformation relation that A point and B are ordered:
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510695469.6A CN105184756B (en) | 2013-09-04 | 2013-09-04 | A kind of fish-eye method for correcting image |
| CN201310395095.7A CN103456171B (en) | 2013-09-04 | 2013-09-04 | A kind of based on fish-eye vehicle flow detection system, method and method for correcting image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310395095.7A CN103456171B (en) | 2013-09-04 | 2013-09-04 | A kind of based on fish-eye vehicle flow detection system, method and method for correcting image |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510695469.6A Division CN105184756B (en) | 2013-09-04 | 2013-09-04 | A kind of fish-eye method for correcting image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103456171A true CN103456171A (en) | 2013-12-18 |
| CN103456171B CN103456171B (en) | 2016-04-06 |
Family
ID=49738489
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510695469.6A Active CN105184756B (en) | 2013-09-04 | 2013-09-04 | A kind of fish-eye method for correcting image |
| CN201310395095.7A Active CN103456171B (en) | 2013-09-04 | 2013-09-04 | A kind of based on fish-eye vehicle flow detection system, method and method for correcting image |
Family Applications Before (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510695469.6A Active CN105184756B (en) | 2013-09-04 | 2013-09-04 | A kind of fish-eye method for correcting image |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN105184756B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104516189A (en) * | 2014-12-26 | 2015-04-15 | 广东光阵光电科技有限公司 | 180-degree wide-angle shooting camera module of VTM machine and shooting method of 180-degree wide-angle shooting camera module |
| CN105989354A (en) * | 2015-05-25 | 2016-10-05 | 乐视移动智能信息技术（北京）有限公司 | Positioning method and system |
| CN106600546A (en) * | 2016-11-14 | 2017-04-26 | Tcl集团股份有限公司 | Ultra-wide angle camera distortion correction method and system |
| CN110556000A (en) * | 2018-06-04 | 2019-12-10 | 义硕智能股份有限公司 | Artificial intelligence traffic detection system |
| TWI712012B (en) * | 2018-06-04 | 2020-12-01 | 義碩智能股份有限公司 | Artificial intelligence traffic detection system |
| CN112441497A (en) * | 2019-08-28 | 2021-03-05 | 东芝电梯株式会社 | User detection system for elevator |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108257102B (en) * | 2018-01-22 | 2020-11-13 | 豪威科技(上海)有限公司 | Fisheye correction system and method |
| US10796402B2 (en) * | 2018-10-19 | 2020-10-06 | Tusimple, Inc. | System and method for fisheye image processing |
| CN113947638B (en) * | 2021-10-04 | 2023-05-05 | 桂林理工大学 | Method for correcting orthographic image of fish-eye camera |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001216598A (en) * | 2000-02-04 | 2001-08-10 | Mitsubishi Electric Corp | Traffic flow measuring instrument |
| JP2005284678A (en) * | 2004-03-29 | 2005-10-13 | Mitsubishi Heavy Ind Ltd | Traffic flow measuring device |
| KR20090053459A (en) * | 2007-11-23 | 2009-05-27 | 렉스젠(주) | A system and method for phtographing the car |
| CN101447075A (en) * | 2008-12-31 | 2009-06-03 | 天津理工大学 | Wide-angle lens-based FPGA & DSP embedded multi-valued targets threshold categorization tracking device |
| CN201690518U (en) * | 2010-01-21 | 2010-12-29 | 天津理工大学 | Embedded fisheye image real-time distortion correction device based on FPGA |
| CN102509098A (en) * | 2011-10-08 | 2012-06-20 | 天津大学 | Fisheye image vehicle identification method |
| KR101277188B1 (en) * | 2012-07-12 | 2013-06-20 | (주)엠로드정보시스템 | Accident sensing system at intersection by using fisheye lens of high-definition |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW594453B (en) * | 2002-07-26 | 2004-06-21 | Gwo-Jen Jan | Method for presenting fisheye-camera images |
| CN101577002B (en) * | 2009-06-16 | 2011-12-28 | 天津理工大学 | Calibration method of fish-eye lens imaging system applied to target detection |
| CN101783011B (en) * | 2010-01-08 | 2011-12-07 | 宁波大学 | Distortion correction method of fish eye lens |
| CN102509261B (en) * | 2011-10-10 | 2014-05-07 | 宁波大学 | Distortion correction method for fisheye lens |
| JP5898475B2 (en) * | 2011-11-28 | 2016-04-06 | クラリオン株式会社 | In-vehicle camera system, calibration method thereof, and calibration program thereof |
| CN102663734A (en) * | 2012-03-15 | 2012-09-12 | 天津理工大学 | Fish eye lens calibration and fish eye image distortion correction method |
-
2013
- 2013-09-04 CN CN201510695469.6A patent/CN105184756B/en active Active
- 2013-09-04 CN CN201310395095.7A patent/CN103456171B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001216598A (en) * | 2000-02-04 | 2001-08-10 | Mitsubishi Electric Corp | Traffic flow measuring instrument |
| JP2005284678A (en) * | 2004-03-29 | 2005-10-13 | Mitsubishi Heavy Ind Ltd | Traffic flow measuring device |
| KR20090053459A (en) * | 2007-11-23 | 2009-05-27 | 렉스젠(주) | A system and method for phtographing the car |
| CN101447075A (en) * | 2008-12-31 | 2009-06-03 | 天津理工大学 | Wide-angle lens-based FPGA & DSP embedded multi-valued targets threshold categorization tracking device |
| CN201690518U (en) * | 2010-01-21 | 2010-12-29 | 天津理工大学 | Embedded fisheye image real-time distortion correction device based on FPGA |
| CN102509098A (en) * | 2011-10-08 | 2012-06-20 | 天津大学 | Fisheye image vehicle identification method |
| KR101277188B1 (en) * | 2012-07-12 | 2013-06-20 | (주)엠로드정보시스템 | Accident sensing system at intersection by using fisheye lens of high-definition |
Non-Patent Citations (3)
| Title |
|---|
| 史忠科 等: "《交通图像采集与分析》", 30 April 2007, article "系统应用", pages: 67-68 * |
| 王永仲: "《鱼眼镜头光学》", 31 August 2006, article "鱼眼镜头光学" * |
| 齐志强: "基于FPGA的全方位视觉图像畸变校正", 《中国优秀硕士学位论文全文数据库信息科技辑》, no. 7, 15 July 2010 (2010-07-15), pages 12 - 20 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104516189A (en) * | 2014-12-26 | 2015-04-15 | 广东光阵光电科技有限公司 | 180-degree wide-angle shooting camera module of VTM machine and shooting method of 180-degree wide-angle shooting camera module |
| CN105989354A (en) * | 2015-05-25 | 2016-10-05 | 乐视移动智能信息技术（北京）有限公司 | Positioning method and system |
| CN106600546A (en) * | 2016-11-14 | 2017-04-26 | Tcl集团股份有限公司 | Ultra-wide angle camera distortion correction method and system |
| CN106600546B (en) * | 2016-11-14 | 2020-12-22 | 深圳市Tcl高新技术开发有限公司 | Distortion correction method and system for ultra-wide-angle camera |
| CN110556000A (en) * | 2018-06-04 | 2019-12-10 | 义硕智能股份有限公司 | Artificial intelligence traffic detection system |
| TWI712012B (en) * | 2018-06-04 | 2020-12-01 | 義碩智能股份有限公司 | Artificial intelligence traffic detection system |
| CN112441497A (en) * | 2019-08-28 | 2021-03-05 | 东芝电梯株式会社 | User detection system for elevator |
| CN112441497B (en) * | 2019-08-28 | 2023-01-10 | 东芝电梯株式会社 | User detection system for elevator |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105184756B (en) | 2018-03-09 |
| CN103456171B (en) | 2016-04-06 |
| CN105184756A (en) | 2015-12-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103456171A (en) | Vehicle flow detection system and method based on fish-eye lens and image correction method | |
| CN112785702B (en) | SLAM method based on tight coupling of 2D laser radar and binocular camera | |
| WO2021004312A1 (en) | Intelligent vehicle trajectory measurement method based on binocular stereo vision system | |
| CN109035200B (en) | Bolt positioning and pose detection method based on single-eye and double-eye vision cooperation | |
| CN107843251B (en) | Pose estimation method of mobile robot | |
| JP2019028967A (en) | Method and device for positioning vehicle on lateral side, storage medium, and program article | |
| CN105551020B (en) | A kind of method and device detecting object size | |
| CN102737236A (en) | Method for automatically acquiring vehicle training sample based on multi-modal sensor data | |
| CN112396656B (en) | Outdoor mobile robot pose estimation method based on fusion of vision and laser radar | |
| WO2015088044A1 (en) | Disparity value deriving device, movable apparatus, robot, disparity value producing method, and computer program | |
| ES2569386T3 (en) | Method and system to process a video image | |
| CN109141255A (en) | A kind of bow net monitoring method | |
| CN107122770A (en) | Many mesh camera systems, intelligent driving system, automobile, method and storage medium | |
| CN114140439A (en) | Laser welding seam feature point identification method and device based on deep learning | |
| CN102402855A (en) | Method and system of fusing real-time panoramic videos of double cameras for intelligent traffic | |
| CN111681283A (en) | Monocular stereoscopic vision-based relative pose calculation method applied to wireless charging alignment | |
| CN102252661A (en) | Globoid three-dimensional positioning method for machine vision | |
| Wang et al. | VIMI: Vehicle-infrastructure multi-view intermediate fusion for camera-based 3D object detection | |
| JP6922348B2 (en) | Information processing equipment, methods, and programs | |
| WO2024131200A1 (en) | Monocular-vision-based vehicle 3d locating method and apparatus, and vehicle | |
| CN110197104B (en) | Distance measurement method and device based on vehicle | |
| WO2015025555A1 (en) | Traffic volume measurement device and traffic volume measurement method | |
| CN104700409A (en) | Method for automatically adjusting preset position of camera in accordance with monitoring target | |
| CN107767366A (en) | A kind of transmission line of electricity approximating method and device | |
| TW202103109A (en) | Method and apparatus for object detection |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| C56 | Change in the name or address of the patentee | ||
| CP01 | Change in the name or title of a patent holder |
Address after: 100081, Beijing, Haidian Haidian District Road, 21, Zhongguancun intellectual property building, block B, 7 Patentee after: BEIJING ITARGE TECHNOLOGIES CO., LTD. Address before: 100081, Beijing, Haidian Haidian District Road, 21, Zhongguancun intellectual property building, block B, 7 Patentee before: Beijing iTARGE Software Technologies Development Co., Ltd. |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: Vehicle flow detection system and method based on fish-eye lens and image correction method Effective date of registration: 20170810 Granted publication date: 20160406 Pledgee: China Co truction Bank Corp Beijing Zhongguancun branch Pledgor: BEIJING ITARGE TECHNOLOGIES CO., LTD.|BEIJING MINGTAIZHI SOFTWARE TECHNOLOGY DEVELOPMENT CO., LTD. Registration number: 2017990000732 |
|
| PC01 | Cancellation of the registration of the contract for pledge of patent right | ||
| PC01 | Cancellation of the registration of the contract for pledge of patent right |
Date of cancellation: 20181116 Granted publication date: 20160406 Pledgee: China Co truction Bank Corp Beijing Zhongguancun branch Pledgor: BEIJING ITARGE TECHNOLOGIES CO., LTD.|BEIJING MINGTAIZHI SOFTWARE TECHNOLOGY DEVELOPMENT CO., LTD. Registration number: 2017990000732 |