CN111256707A - Congestion car following system and terminal based on look around - Google Patents
Congestion car following system and terminal based on look around Download PDFInfo
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Abstract
The invention provides a congestion following system and a terminal based on look-around.A look-around image acquisition module is used for detecting a target vehicle and wheels of the target vehicle in an image by using a target detection algorithm; the vehicle wheel matching module is used for judging the subordination relationship between the vehicle and the wheels so as to match the vehicle with the wheels; the vehicle numbering module is used for giving consistent ID to the target vehicles matched by the vehicle wheel matching module in a continuous time sequence by using the same target vehicle; the relative position calculation module obtains the position of the grounding point of the wheel in the image coordinate system according to the detected contact information of the wheel and the ground, and obtains the position of the target vehicle relative to the vehicle in the detection range. The invention can judge the position and motion information of the vehicle with the jam, thereby planning and controlling the running track, speed and the like of the vehicle, avoiding the vehicle collision, relieving the traffic jam condition and improving the running efficiency.
Description
Technical Field
The invention relates to the technical field of automotive electronics, in particular to a system and a terminal for tracking a vehicle in a congestion mode based on look-around.
Background
The measurement of the distance between vehicles provides extremely important information for vehicle-to-vehicle communication on roads or automatic/auxiliary driving, almost all vehicle early warning types need to take vehicle distance measurement as a key input parameter, such as lowest-level lane departure early warning, the precision requirement is that the distance from the wheels of the vehicle to the inner edge and the outer edge of a lane line is calculated below 10cm, and the advanced similar vehicle collision early warning function needs to calculate the distance from the vehicle to the surrounding vehicles quickly in real time.
Therefore, a system and a terminal for tracking the vehicle in the congestion based on the look-around are provided.
Disclosure of Invention
In order to solve the above and other potential technical problems, the invention provides a Traffic jam following system and a terminal based on look-around, which are used for judging the position and motion information of a jammed vehicle by using a Traffic jam following (TJP) function developed by the vehicle in Traffic jam working conditions such as high speed and urban expressway and the like, so that the running track, speed and the like of the vehicle can be planned and controlled, the vehicle collision can be avoided, the Traffic jam condition can be relieved, and the running efficiency can be improved.
A system for tracking a vehicle based on looking around congestion comprises:
the system comprises a panoramic image acquisition module, a panoramic image acquisition module and a panoramic image acquisition module, wherein the panoramic image acquisition module is used for detecting a target vehicle and wheels of the target vehicle in an image by using a target detection algorithm;
the vehicle wheel matching module is used for judging the subordination relationship between the vehicle and the wheels so as to match the vehicle and the wheels;
the vehicle numbering module is used for giving consistent IDs to the matched target vehicles of the vehicle wheel matching module in a continuous time sequence by using the same target vehicle;
and the relative position calculation module is used for acquiring the position of the grounding point of the wheel in the image coordinate system according to the detected contact information of the wheel and the ground, and acquiring the position of the target vehicle relative to the vehicle in the detection range.
The vehicle congestion control device further comprises a congestion judging module which judges the congestion intention of the target vehicle corresponding to the ID according to the position change of the target vehicle relative to the vehicle in continuous time, which is obtained by the relative position calculating module.
Furthermore, the all-round looking image collected by the input vehicle of the all-round looking image collecting module is used for collecting at least two camera data, fusing two paths of camera data to form a complete all-round looking image, and detecting the target vehicle and wheels of each target vehicle by using the all-round looking image.
Furthermore, when the all-round-looking image acquisition module acquires at least two camera data and fuses the two camera data to form a complete all-round-looking image, the two camera data mark a uniform time point with a timestamp so that the two fused cameras capture the consistency of the image on the time point.
Further, in the vehicle wheel matching module, the dependency relationship between the target vehicle and the wheels is determined, and the vehicle and the wheels are matched by using an example-level detection method or other registration schemes, which includes three cases:
a, successfully matching a target vehicle with a front wheel;
b, successfully matching the target vehicle with the rear wheels;
c, successfully matching the target vehicle with the front wheel and the rear wheel;
and when the matching with the single wheel is successful, the position of the other wheel can be predicted by using Kalman filtering.
The vehicle grounding point acquisition module is used for acquiring the grounding point information of the target vehicle, projecting the grounding point information of the target vehicle into a physical coordinate system, performing error correction fitting through the error correction fitting module, and then acquiring the position of the vehicle relative to the vehicle.
Furthermore, the error correction fitting module is used for re-calibration and reducing errors of re-projection, including errors caused by optical center offset, barrel distortion of a fish eye diagram and the like.
Further, the error correction fitting module includes, but is not limited to, two ways:
the input is image coordinates, and the expression form of the output fitting target is the difference between a physical true value and the input image coordinates when the physical true value is projected on an image.
b, inputting an image coordinate, and outputting an expression form of the fitting target as a difference between a physical true value and a world coordinate projected by the input image coordinate.
Further, the error correction fitting module includes, but is not limited to, a first fitting module, a second fitting module, and a verification module; the first fitting module is rough fitting; the second fitting module is a fine fitting, and the verification module is used for verifying the fitting accuracy of the coarse fitting and/or the fine fitting.
Further, the first fitting module uses a vehicle coordinate system as an origin, uses image coordinates with calibration characteristics and physical coordinates acquired by a camera as references, finds a fitting method of coordinate relation errors between the locomotive origin and the calibration characteristics, and determines the coordinate relation errors between the locomotive origin and the calibration characteristics so as to correct fitting parameters.
Further, the second fitting module uses the first fitting module again for fitting every time the vehicle moves a preset distance from the current position.
Further, the specific implementation manner of the second fitting module is that the driving vehicle moves forward, leftward and rightward by 10cm respectively in the environment of the calibration features, the images are collected after the movement, a fitting method of a vehicle head origin and a calibration feature error is found by taking the image coordinates and physical groups with the calibration features collected by the camera as references, and a coordinate relation error between the vehicle head origin and the calibration features is determined, so that fitting parameters are corrected, and fitting data in a preset range are obtained.
Further, the verification module comprises the verification of the correctness of the annotation data and the verification of the correctness of the projection error fitting curved surface.
Further, the verification of correctness of the annotation data comprises: and generating a file for fitting errors and an error analysis file in the vehicle-mounted system, and checking whether the corresponding label is wrong or not when the error value in the error analysis file is more than 1-4 times of the preset value.
Further, the verifying the correctness of the projection error fitting surface in the verification module comprises:
s0501: verifying the checkerboard data;
s0502: verifying RTK true value data;
s0503: and (4) respectively passing the detection point and the artificial marking grounding point on the image through a fitting curved surface, then solving the difference and looking at the error.
As a preferred embodiment, the image coordinates of real points and fitting points in the image are labeled, projected to world coordinates, and then errors are calculated. The point near the tail of the vehicle is a fitting point, and the point near the head of the vehicle is a real grounding point.
Furthermore, the system also comprises a region-of-interest screening module, wherein the region-of-interest screening module filters a region-of-interest (ROI) of the grounding point information of the target vehicle, and the ROI range is relatively large so as to give certain reaction time to the subsequent Kalman filtering process.
And further, the grounding point screening module is used for reserving the grounding point within the ROI of the target vehicle, namely corresponding grounding point information of the reserved target vehicle is obtained, and the grounding point outside the ROI of the target vehicle is omitted.
And the data output module performs Kalman filtering according to the ReiD and the grounding point information of the target vehicle to obtain the speed and the course angle of the target vehicle and the position information of the grounding point of the wheel.
Further, the course angle is calculated according to the position of the rear wheel of the target vehicle, and ROI filtering is carried out on the information, wherein the ROI area at the moment is set to be about one half of the ROI range in the vehicle numbering module; the information in the ROI range is the final output result; for targets outside the ROI, it is discarded.
And the target vehicle information repairing module can be used for completing the rest information of the target vehicle according to the heading angle and the grounding point positions of the front and rear wheels to obtain the position coordinates of the complete target vehicle.
As described above, the present invention has the following advantageous effects:
1) by detecting the wheels, the grounding point information of the wheels is obtained, and the motion information of the adjacent vehicle can be accurately judged.
2) The vehicle and the wheel are matched by adopting the example-level detection method, the subordination relationship between the vehicle and the wheel can be accurately judged, and the realization of the vehicle ReID function is facilitated.
3) And the error of the re-projection is reduced by adopting projection error fitting and re-calibration, wherein the error comprises errors caused by optical center offset, barrel-shaped distortion of a fisheye diagram and the like.
4) The projection error fitting ensures the fitting accuracy by rough fitting, fine fitting and verification.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 shows a flow chart of the present invention.
FIG. 2 is a flow chart of another embodiment of the present invention.
Fig. 3 shows a flow chart of the matching of a target vehicle and a vehicle according to the present invention.
FIG. 4 shows a flow chart of the error correction fitting and region of interest filtering of the present invention.
FIG. 5 is a schematic representation of the lower edge of a target vehicle of the present invention as it would contact the ground and the contact point after error fitting.
FIG. 6 is a schematic representation of the lower edge of a target vehicle of the present invention prior to error fitting with the ground contact point.
FIG. 7 is a schematic representation of the error fit of the lower edge of the subject vehicle to the ground contact point.
FIG. 8 is a schematic representation of a lower edge of a target vehicle prior to error fitting with a ground contact point in accordance with another embodiment of the present invention.
FIG. 9 is a schematic diagram of an error fit of the lower edge of the target vehicle to the ground contact point in accordance with another embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be understood that the structures, ratios, sizes, and the like shown in the drawings and described in the specification are only used for matching with the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the conditions under which the present invention can be implemented, so that the present invention has no technical significance, and any structural modification, ratio relationship change, or size adjustment should still fall within the scope of the present invention without affecting the efficacy and the achievable purpose of the present invention. In addition, the terms "upper", "lower", "left", "right", "middle" and "one" used in the present specification are for clarity of description, and are not intended to limit the scope of the present invention, and the relative relationship between the terms and the terms is not to be construed as a scope of the present invention.
With reference to figures 1 to 9 of the drawings,
a system for tracking a vehicle based on looking around congestion comprises:
the system comprises a panoramic image acquisition module, a panoramic image acquisition module and a panoramic image acquisition module, wherein the panoramic image acquisition module is used for detecting a target vehicle and wheels of the target vehicle in an image by using a target detection algorithm;
the vehicle wheel matching module is used for judging the subordination relationship between the vehicle and the wheels so as to match the vehicle and the wheels;
the vehicle numbering module is used for giving consistent IDs to the matched target vehicles of the vehicle wheel matching module in a continuous time sequence by using the same target vehicle;
and the relative position calculation module is used for acquiring the position of the grounding point of the wheel in the image coordinate system according to the detected contact information of the wheel and the ground, and acquiring the position of the target vehicle relative to the vehicle in the detection range.
The vehicle congestion control device further comprises a congestion judging module which judges the congestion intention of the target vehicle corresponding to the ID according to the position change of the target vehicle relative to the vehicle in continuous time, which is obtained by the relative position calculating module.
Furthermore, the all-round looking image collected by the input vehicle of the all-round looking image collecting module is used for collecting at least two camera data, fusing two paths of camera data to form a complete all-round looking image, and detecting the target vehicle and wheels of each target vehicle by using the all-round looking image.
Furthermore, when the all-round-looking image acquisition module acquires at least two camera data and fuses the two camera data to form a complete all-round-looking image, the two camera data mark a uniform time point with a timestamp so that the two fused cameras capture the consistency of the image on the time point.
Further, in the vehicle wheel matching module, the dependency relationship between the target vehicle and the wheels is determined, and the vehicle and the wheels are matched by using an example-level detection method or other registration schemes, which includes three cases:
a, successfully matching a target vehicle with a front wheel;
b, successfully matching the target vehicle with the rear wheels;
c, successfully matching the target vehicle with the front wheel and the rear wheel;
and when the matching with the single wheel is successful, the position of the other wheel can be predicted by using Kalman filtering.
The vehicle grounding point acquisition module is used for acquiring the grounding point information of the target vehicle, projecting the grounding point information of the target vehicle into a physical coordinate system, performing error correction fitting through the error correction fitting module, and then acquiring the position of the vehicle relative to the vehicle.
Furthermore, the error correction fitting module is used for re-calibration and reducing errors of re-projection, including errors caused by optical center offset, barrel distortion of a fish eye diagram and the like.
Further, the error correction fitting module includes, but is not limited to, two ways:
the input is image coordinates, and the expression form of the output fitting target is the difference between a physical true value and the input image coordinates when the physical true value is projected on an image.
b, inputting an image coordinate, and outputting an expression form of the fitting target as a difference between a physical true value and a world coordinate projected by the input image coordinate.
Further, the error correction fitting module includes, but is not limited to, a first fitting module, a second fitting module, and a verification module; the first fitting module is rough fitting; the second fitting module is a fine fitting, and the verification module is used for verifying the fitting accuracy of the coarse fitting and/or the fine fitting.
Further, the first fitting module uses a vehicle coordinate system as an origin, uses image coordinates with calibration characteristics and physical coordinates acquired by a camera as references, finds a fitting method of coordinate relation errors between the locomotive origin and the calibration characteristics, and determines the coordinate relation errors between the locomotive origin and the calibration characteristics so as to correct fitting parameters.
Further, the second fitting module uses the first fitting module again for fitting every time the vehicle moves a preset distance from the current position.
Further, the specific implementation manner of the second fitting module is that the driving vehicle moves forward, leftward and rightward by 10cm respectively in the environment of the calibration features, the images are collected after the movement, a fitting method of a vehicle head origin and a calibration feature error is found by taking the image coordinates and physical groups with the calibration features collected by the camera as references, and a coordinate relation error between the vehicle head origin and the calibration features is determined, so that fitting parameters are corrected, and fitting data in a preset range are obtained.
Further, the verification module comprises the verification of the correctness of the annotation data and the verification of the correctness of the projection error fitting curved surface.
Further, the verification of correctness of the annotation data comprises: and generating a file for fitting errors and an error analysis file in the vehicle-mounted system, and checking whether the corresponding label is wrong or not when the error value in the error analysis file is more than 1-4 times of the preset value.
Further, the verifying the correctness of the projection error fitting surface in the verification module comprises:
s0501: verifying the checkerboard data;
s0502: verifying RTK true value data;
s0503: and (4) respectively passing the detection point and the artificial marking grounding point on the image through a fitting curved surface, then solving the difference and looking at the error.
As a preferred embodiment, the image coordinates of real points and fitting points in the image are labeled, projected to world coordinates, and then errors are calculated. The point near the tail of the vehicle is a fitting point, and the point near the head of the vehicle is a real grounding point.
Furthermore, the system also comprises a region-of-interest screening module, wherein the region-of-interest screening module filters a region-of-interest (ROI) of the grounding point information of the target vehicle, and the ROI range is relatively large so as to give certain reaction time to the subsequent Kalman filtering process.
And further, the grounding point screening module is used for reserving the grounding point within the ROI of the target vehicle, namely corresponding grounding point information of the reserved target vehicle is obtained, and the grounding point outside the ROI of the target vehicle is omitted.
And the data output module performs Kalman filtering according to the ReiD and the grounding point information of the target vehicle to obtain the speed and the course angle of the target vehicle and the position information of the grounding point of the wheel.
Further, the course angle is calculated according to the position of the rear wheel of the target vehicle, and ROI filtering is carried out on the information, wherein the ROI area at the moment is set to be about one half of the ROI range in the vehicle numbering module; the information in the ROI range is the final output result; for targets outside the ROI, it is discarded.
And the target vehicle information repairing module can be used for completing the rest information of the target vehicle according to the heading angle and the grounding point positions of the front and rear wheels to obtain the position coordinates of the complete target vehicle.
As a preferred embodiment, the present embodiment further provides a terminal device, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack-mounted cloud, a blade-type cloud, a tower-type cloud, or a rack-type cloud (including an independent cloud or a cloud cluster composed of multiple clouds) capable of executing a program. The terminal device of this embodiment at least includes but is not limited to: a memory, a processor communicatively coupled to each other via a system bus. It is noted that a terminal device having a component memory, a processor, but it is understood that not all of the illustrated components are required to be implemented, and that more or fewer components may be implemented in alternative look-around based congestion following systems.
As a preferred embodiment, the memory (i.e., readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. In other embodiments, the memory may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), etc. provided on the computer device. Of course, the memory may also include both internal and external storage devices for the computer device. In this embodiment, the memory is generally configured to store an operating system and various types of application software installed in the computer device, for example, a program code of a congestion following method based on look-around in the embodiment, and the like. In addition, the memory may also be used to temporarily store various types of data that have been output or are to be output.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention shall be covered by the claims of the present invention.
Claims (8)
1. A system for tracking a vehicle in a congestion manner based on a look-around manner, comprising:
the system comprises a panoramic image acquisition module, a panoramic image acquisition module and a panoramic image acquisition module, wherein the panoramic image acquisition module is used for detecting a target vehicle and wheels of the target vehicle in an image by using a target detection algorithm;
the vehicle wheel matching module is used for judging the subordination relationship between the vehicle and the wheels so as to match the vehicle and the wheels;
the vehicle numbering module is used for giving consistent IDs to the matched target vehicles of the vehicle wheel matching module in a continuous time sequence by using the same target vehicle;
and the relative position calculation module is used for acquiring the position of the grounding point of the wheel in the image coordinate system according to the detected contact information of the wheel and the ground, and acquiring the position of the target vehicle relative to the vehicle in the detection range.
2. The look-around based congestion following system according to claim 1, wherein the error correction fitting module comprises but is not limited to a first fitting module, a second fitting module and a verification module; the first fitting module is rough fitting; the second fitting module is a fine fitting, and the verification module is used for verifying the fitting accuracy of the coarse fitting and/or the fine fitting.
3. The system of claim 1, further comprising a region-of-interest (ROI) filtering module, wherein the ROI filtering module is used for filtering ROI (region of interest) of grounding point information of a target vehicle, and the ROI range is relatively large so as to give a certain reaction time to a subsequent Kalman filtering process.
4. The system of claim 1, further comprising a ground point screening module, wherein the ground point screening module retains the ground points within the ROI of the target vehicle, that is, retains the corresponding ground point information of the target vehicle, and discards the ground points outside the ROI of the target vehicle.
5. The system of claim 1, further comprising a data output module, wherein the data output module performs kalman filtering on the ReID and the ground point information of the target vehicle to obtain the speed and the heading angle of the target vehicle and the position information of the wheel ground point.
6. The system of claim 5, wherein the course angle is calculated according to the position of the rear wheel of the target vehicle, and the information is subjected to ROI filtering, wherein the ROI area is set to be about one-half of the ROI range in the vehicle number module; the information in the ROI range is the final output result; for targets outside the ROI, it is discarded.
7. The system of claim 6, further comprising a target vehicle information recovery module, wherein the target vehicle information recovery module is capable of completing the rest of the information of the target vehicle according to the position of the ground point of the front and rear wheels and the heading angle, and obtaining the position coordinates of the complete target vehicle, for the output target vehicle information in the data output module including but not limited to the speed, the heading angle, and the ground point of the wheels.
8. A mobile terminal, characterized by: which may be a vehicle-mounted terminal or a mobile phone mobile terminal, implementing the vehicle-mounted terminal or the mobile phone mobile terminal of the around-the-sight based congestion following system of claims 1-7.
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| CN110539748A (en) * | 2019-08-27 | 2019-12-06 | 北京纵目安驰智能科技有限公司 | congestion car following system and terminal based on look around |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5396559B1 (en) * | 2013-04-17 | 2014-01-22 | 学校法人 埼玉医科大学 | Image encoding apparatus, image encoding method, image encoding program, and recording medium |
| CN103950410A (en) * | 2014-04-29 | 2014-07-30 | 深圳前向启创数码技术有限公司 | Panoramic auxiliary driving method and system |
| CN104392212A (en) * | 2014-11-14 | 2015-03-04 | 北京工业大学 | Method for detecting road information and identifying forward vehicles based on vision |
| WO2015114654A1 (en) * | 2014-01-17 | 2015-08-06 | Kpit Technologies Ltd. | Vehicle detection system and method thereof |
| WO2015172735A1 (en) * | 2014-05-16 | 2015-11-19 | Mediatek Inc. | Detection devices and methods for detecting regions of interest |
| CN105223583A (en) * | 2015-09-10 | 2016-01-06 | 清华大学 | A kind of target vehicle course angle computing method based on three-dimensional laser radar |
| CN106127113A (en) * | 2016-06-15 | 2016-11-16 | 北京联合大学 | A kind of road track line detecting method based on three-dimensional laser radar |
| CN107389088A (en) * | 2017-05-27 | 2017-11-24 | 纵目科技（上海）股份有限公司 | Error correcting method, device, medium and the equipment of vehicle-mounted inertial navigation |
| CN107463890A (en) * | 2017-07-20 | 2017-12-12 | 浙江零跑科技有限公司 | A kind of Foregut fermenters and tracking based on monocular forward sight camera |
| CN109002840A (en) * | 2018-06-26 | 2018-12-14 | 北京纵目安驰智能科技有限公司 | One kind being based on cascade semantic segmentation method, system, terminal and storage medium |
| CN109118519A (en) * | 2018-07-26 | 2019-01-01 | 北京纵目安驰智能科技有限公司 | Target Re-ID method, system, terminal and the storage medium of Case-based Reasoning segmentation |
| CN109754434A (en) * | 2018-12-27 | 2019-05-14 | 歌尔科技有限公司 | Camera calibration method, apparatus, user equipment and storage medium |
| CN110065494A (en) * | 2019-04-09 | 2019-07-30 | 魔视智能科技(上海)有限公司 | A kind of vehicle collision avoidance method based on wheel detection |
| CN110077399A (en) * | 2019-04-09 | 2019-08-02 | 魔视智能科技(上海)有限公司 | A kind of vehicle collision avoidance method merged based on roadmarking, wheel detection |
-
2019
- 2019-08-27 CN CN201910798894.6A patent/CN111256707A/en active Pending
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5396559B1 (en) * | 2013-04-17 | 2014-01-22 | 学校法人 埼玉医科大学 | Image encoding apparatus, image encoding method, image encoding program, and recording medium |
| WO2015114654A1 (en) * | 2014-01-17 | 2015-08-06 | Kpit Technologies Ltd. | Vehicle detection system and method thereof |
| CN103950410A (en) * | 2014-04-29 | 2014-07-30 | 深圳前向启创数码技术有限公司 | Panoramic auxiliary driving method and system |
| WO2015172735A1 (en) * | 2014-05-16 | 2015-11-19 | Mediatek Inc. | Detection devices and methods for detecting regions of interest |
| CN104392212A (en) * | 2014-11-14 | 2015-03-04 | 北京工业大学 | Method for detecting road information and identifying forward vehicles based on vision |
| CN105223583A (en) * | 2015-09-10 | 2016-01-06 | 清华大学 | A kind of target vehicle course angle computing method based on three-dimensional laser radar |
| CN106127113A (en) * | 2016-06-15 | 2016-11-16 | 北京联合大学 | A kind of road track line detecting method based on three-dimensional laser radar |
| CN107389088A (en) * | 2017-05-27 | 2017-11-24 | 纵目科技（上海）股份有限公司 | Error correcting method, device, medium and the equipment of vehicle-mounted inertial navigation |
| CN107463890A (en) * | 2017-07-20 | 2017-12-12 | 浙江零跑科技有限公司 | A kind of Foregut fermenters and tracking based on monocular forward sight camera |
| CN109002840A (en) * | 2018-06-26 | 2018-12-14 | 北京纵目安驰智能科技有限公司 | One kind being based on cascade semantic segmentation method, system, terminal and storage medium |
| CN109118519A (en) * | 2018-07-26 | 2019-01-01 | 北京纵目安驰智能科技有限公司 | Target Re-ID method, system, terminal and the storage medium of Case-based Reasoning segmentation |
| CN109754434A (en) * | 2018-12-27 | 2019-05-14 | 歌尔科技有限公司 | Camera calibration method, apparatus, user equipment and storage medium |
| CN110065494A (en) * | 2019-04-09 | 2019-07-30 | 魔视智能科技(上海)有限公司 | A kind of vehicle collision avoidance method based on wheel detection |
| CN110077399A (en) * | 2019-04-09 | 2019-08-02 | 魔视智能科技(上海)有限公司 | A kind of vehicle collision avoidance method merged based on roadmarking, wheel detection |
Non-Patent Citations (3)
| Title |
|---|
| 周静, 田金文: "基于海岸线背景的海面小目标检测方法", 红外与激光工程, no. 04, 25 August 2005 (2005-08-25), pages 486 - 489 * |
| 段建民;刘冠宇;郑榜贵;: "基于视觉及多特征的前方车辆检测算法", 北京工业大学学报, no. 09, pages 1326 - 1333 * |
| 薛玉利;: "雾霾天气情况下的交通标志检测", 交通运输系统工程与信息, no. 04, 15 August 2016 (2016-08-15), pages 88 - 94 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110539748A (en) * | 2019-08-27 | 2019-12-06 | 北京纵目安驰智能科技有限公司 | congestion car following system and terminal based on look around |
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