CN101344965A - Tracking system based on binocular camera shooting - Google Patents

Tracking system based on binocular camera shooting Download PDF

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Publication number
CN101344965A
CN101344965A CNA2008100424910A CN200810042491A CN101344965A CN 101344965 A CN101344965 A CN 101344965A CN A2008100424910 A CNA2008100424910 A CN A2008100424910A CN 200810042491 A CN200810042491 A CN 200810042491A CN 101344965 A CN101344965 A CN 101344965A
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module
camera
coordinate
unique point
target
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赵宇明
胡福乔
蔡岭
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Shanghai Jiaotong University
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Shanghai Jiaotong University
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Abstract

The invention relates to a full automatic target detecting and tracking system in the computer vision field, wherein, an input module is responsible for collecting digital images shot by a binocular camera to be taken as system input, the obtained digital images are input into a feature extraction module and feature analysis is carried out to one image to obtain a plurality of characteristic points to be taken as the subsequently processed images. By matching the characteristic points of two images, the parallax of the two images is calculated, and by combining the pre-informed external and internal parameters of the camera, the lower coordinate of a camera coordinate system of the characteristic points can be calculated; furthermore, by the relationship between a world coordinate system and the camera coordinate system, the coordinate of the world coordinate system of the characteristic points can be known. A clustering module clusters the characteristic points into an aggregation for expressing target position, while a trajectory analysis module estimates the target position on a time sequence to obtain the motion trajectory of the target. The invention can effectively and steadily detect the targets in a designated area, track the targets and calculate the motion trajectories of the targets.

Description

Tracker based on binocular camera shooting
Technical field
What the present invention relates to is the tracker in a kind of image recognition technique field, specifically, is a kind of tracker based on binocular camera shooting.
Background technology
Along with popularizing of digital camera head, digital picture has been occupied more and more important position in producing and living.Particularly in the monitoring security, digital picture has played vital role at aspects such as Target Recognition and target followings.Because the variation at monitoring scene illumination condition, visual angle has influenced the accuracy of monitoring, feasible full-automatic tracking based on image technique is used and is restricted then.Real-life monitoring scene changes very greatly, and different scenes are very big in the factor difference such as influence, camera angle, target occlusion and shade of ambient lighting.Even in some outdoor scenes, these conditions are different also to be not quite similar constantly.Supervisory system based on single camera is often very responsive to these factors, and accuracy is lower or detection speed is slower in the tracking of real scene.Therefore such tracker is difficult to finish the tracing task in the real scene.At this problem disposal route commonly used is to adopt dynamic background modeling or carry out the machine learning of large sample at specific objective; these two kinds of disposal routes have certain effect for periodic change of background and single target then, contrast can't handling of violent change of background and a plurality of complex targets.
Find through literature search prior art, Tao Zhao, Manoj Aggarwal, Rakesh Kumarand Harpeet Sawhney is at " IEEE Computer Society Conference on Computer Visionand Pattern Recognition " (international institute of electrical and electronic engineers computer vision and pattern-recognition meeting in 2005) literary composition " Real-time Wide Area Muti-Camera Stereo Tracking " (the real-time three-dimensional tracking of developing zone multiple-camera), a kind of system based on single camera and multiple-camera fusion is proposed in this article, single camera is at self visual angle detection and tracking human body, and the multiple-camera Fusion Module makes up all part tracking for same human body, forms global follow.Many people that application volume segmentation and tracking are handled under complex background in detection move, and utilize the fusion method of spatio-temporal constraint to carry out overall treatment for multiple-camera.
The deficiency of said system is: though this system can a plurality of human body targets of detection and tracking, this algorithm need cause hardware cost higher than multiple-camera (being 12 pairs of stereo cameras in the literary composition).Simultaneously algorithm has no idea to calculate the movement velocity and the direction of target, simultaneously can only be and can not be at the detection and tracking of arbitrary object at human body, and the detection and tracking of getting on the car as highway.
Summary of the invention
The objective of the invention is to overcome in the prior art and change the deficiency that the tracker performance is existed for monitoring scene, provide a kind of based on the full automatic Target Tracking System of binocular, make its automatic calculated characteristics of image of taking simultaneously put the distance of video camera according to dual camera, thereby and restore the movement locus of the real world coordinates position evaluating objects of target, can be applied to the target following of real scene and the estimation of target density and direction of motion.
The present invention is achieved by the following technical solutions, the present invention includes following module: load module, characteristic extracting module, disparity estimation module, world coordinate system computing module, target cluster module, trajectory analysis module, load module is responsible for gathering the image that the binocular camera shooting system takes and is imported as system, and the left and right sides image that is obtained is input to characteristic extracting module.Characteristic extracting module is to the object of input picture extract minutiae as subsequent treatment.The unique point that the disparity estimation module extracts for characteristic extracting module is according to the volume coordinate of calculation of parameter unique point in camera coordinates system of camera.It is coordinate that the world coordinate system computing module is converted to real world coordinates again with the coordinate of unique point in camera coordinates system.Target cluster module aggregates into a set of representing real-world object in the space according to the unique point coordinate with a plurality of unique points.The trajectory analysis module provides the movement locus of target in real space in conjunction with the position of current goal set and historical goal set.
Described load module is meant: be responsible for to gather the digital picture of binocular camera shooting system, described digital picture is that digital camera and digital scanner image that can obtain and Digital Video provide the frame in the sequence image.
Described characteristic extracting module is meant: the eigenwert of the matrix that each neighborhood of pixels during input picture is calculated is formed, when surpassing the threshold value that pre-sets, then this point is considered to the unique point in the image.
Described disparity estimation module is meant: the unique point that extracts is sought corresponding same point in another input figure.Because the difference at camera visual angle, the left and right sides, same in the image of the left and right sides image coordinate difference of correspondence, and this difference reaction the difference of its locus in camera coordinate system.The coordinate position of matching characteristic o'clock in two figure, in conjunction with parameters of pick-up head, its position in camera coordinate system can be estimated accurately.
Described world coordinate system computing module is meant: the unique point of extracting processing needs a nearlyer step change into world coordinate system after the coordinate that has obtained under the camera coordinates system.Two coordinate systems are after carrying out some couplings before system's operation, and the mapping relations of both relations can obtain.By these mapping relations, the coordinate of unique point in world coordinate system also is as can be known.
Described target cluster module is meant: will be in world coordinate system all unique points aggregate into several set according to its height and position, the actual position of target in the space of these set correspondences.
Described trajectory analysis module is meant: target is after the volume coordinate of every frame is expressed with set, and these discrete location points are determined the real motion track of whole target in the space by model.
The present invention adopts the method for computer vision in tracking, experimental analysis can determine that the motion of target in the space and the track that calculates meet substantially.Therefore this method can be carried out Tracking Recognition to the target in the captured area of space by the binocular camera shooting head.
The present invention at first imports by two captured width of cloth digital pictures of binocular camera shooting head, is according to the judging characteristic point with the matrix computations eigenwert in each neighborhood of pixels scope.Unique point under image coordinate system can obtain its coordinate under camera coordinates system by the parallax that calculates in two width of cloth figure, and this coordinate can change into world coordinates by the world coordinate system of demarcating in advance.The cluster module aggregates into discrete set with these unique points and expresses the real space object, and last trajectory analysis model connects into movement locus with target in difference position constantly.
The present invention can not only detect target under the violent environment of illumination variation, the movement locus of tracking target, and can access the movement velocity of target and the target density in zone.Compare with general detection tracking, the track that obtains extraterrestrial target that can be more accurate, more stable, simultaneously detection speed also can satisfy normal application, such characteristics make its monitor in public places with stream of people's wagon flow statistics in all have wide practical use.The present invention is based on the basis of computer stereo vision theory, added pattern-recognition, optimization method scheduling theory knowledge is becoming illumination, is stably obtained the movement locus of extraterrestrial target under the conditions such as complex background.
Description of drawings
Fig. 1 is a system architecture diagram of the present invention;
Fig. 2 is the processing flow chart of the embodiment of the invention;
Fig. 3 application example synoptic diagram of the present invention.
Embodiment
Below in conjunction with accompanying drawing embodiments of the invention are elaborated: present embodiment has provided detailed embodiment being to implement under the prerequisite with the technical solution of the present invention, but protection scope of the present invention is not limited to following embodiment.
As shown in Figure 1, present embodiment comprises: load module, and characteristic extracting module, the disparity estimation module, the world coordinate system computing module, target cluster module, the trajectory analysis module, wherein:
Described load module is responsible for gathering the image that the binocular camera shooting system takes and is imported as system, and the left and right sides image that is obtained is input to characteristic extracting module;
Described characteristic extracting module is to the object of input picture extract minutiae as subsequent treatment;
The unique point that described disparity estimation module extracts for characteristic extracting module is according to the volume coordinate of calculation of parameter unique point in camera coordinates system of camera;
It is coordinate that described world coordinate system computing module is converted to real world coordinates again with the coordinate of unique point in camera coordinates system;
Described target cluster module aggregates into a set of representing real-world object in the space according to the unique point coordinate with a plurality of unique points;
Described trajectory analysis module provides the movement locus of target in real space in conjunction with the position of current goal set and historical goal set.
Described load module is responsible for gathering the digital picture of binocular camera shooting system, described digital picture is that digital camera and digital scanner image that can obtain and Digital Video provide the frame in the sequence image, each pixel value of photographic images is deposited in the internal storage location of region of memory correspondence in order, if input picture is a coloured image, then coloured image will be divided into R, G, three passages of B are preserved respectively.
The eigenwert of the matrix that each neighborhood of pixels during described characteristic extracting module is calculated input picture is formed, when surpassing the threshold value that pre-sets, then this point is considered to the unique point in the image.
Described disparity estimation module is sought corresponding same point to the unique point that extracts in another input figure, the coordinate position of matching characteristic o'clock in two figure, in conjunction with parameters of pick-up head, its position in camera coordinate system can be estimated accurately, wherein match-on criterion is as contrast differences distance between the matrix of central point by NCC algorithm computation unique point, by the difference of position o'clock in two images in the real world is parallax, by obtaining parallax, demarcate the inside that obtains camera in advance in conjunction with two cameras, external parameter, with unique point x in image, y coordinate, parallax is converted to the coordinate figure under the camera coordinates.
Described world coordinate system computing module is after the unique point of extract handling has obtained coordinate under the camera coordinates system, with the camera coordinates of unique point be coordinate again in conjunction with the relation of the world coordinate system of demarcating in advance with camera coordinates system, it is to project in the world coordinate system that unique point is had camera coordinates again.
The unique point that described target cluster module is will be in world coordinate system all aggregates into several set according to its height and position, the actual position of target in the space of these set correspondences.
Described trajectory analysis module is meant: target is after the volume coordinate of every frame is expressed with set, and these discrete location points are determined the real motion track of whole target in the space by model.
As shown in Figure 2, present embodiment system handles process flow diagram.The image of two shot by camera about at first being read by load module, characteristic extracting module is calculated unique point among a figure therein, seeks corresponding point then in another figure.The disparity estimation module is according to the difference of 2 o'clock positions in two figure matching, and the further coordinate position of calculated characteristics point in camera coordinates is of combining camera parameter.It is coordinate that the world coordinate system computing module is converted to real world coordinates again with the coordinate of unique point in camera coordinates system; Target cluster module can be clustered into the unique point that is transformed into world coordinates some set by the camera coordinates system of coupling and the relation between the world coordinate system in advance by clustering algorithm.At last, the track module provides the real trace that it moves according to target position and current position in the past in the space.
As shown in Figure 3, the image of two shot by camera about present embodiment at first reads in.Calculate unique point among a figure therein then.Mate coordinate under the computing camera coordinate system with another figure by these unique points.Again with these spot projections in world coordinate system, the cluster that can obtain some points is by projection, these different clusters with the different gray scale sign of the depths after, can obtain the position of target in the monitoring space.By the processing to every frame, tracking module can obtain the real motion track of target.Main window among Fig. 3 shows the left and right sides image that the shooting of binocular camera shooting head obtains, show the unique point of extracting during operation in real time, the window of newly jumping out " Cam " shows that in real time ground surveillance zone, unique point are at the projection on ground and the cluster result and the pursuit path of unique point.

Claims (7)

1, a kind ofly can detect the target of appointed area and, it is characterized in that, comprise the system that it is followed the tracks of: load module, characteristic extracting module, disparity estimation module, world coordinate system computing module, target cluster module, trajectory analysis module, wherein:
Described load module is responsible for gathering the image that the binocular camera shooting system takes and is imported as system, and the left and right sides image that is obtained is input to characteristic extracting module;
Described characteristic extracting module is to the object of input picture extract minutiae as subsequent treatment;
The unique point that described disparity estimation module extracts for characteristic extracting module is according to the volume coordinate of calculation of parameter unique point in camera coordinates system of camera;
It is coordinate that described world coordinate system computing module is converted to real world coordinates again with the coordinate of unique point in camera coordinates system;
Described target cluster module aggregates into a set of representing real-world object in the space according to the unique point coordinate with a plurality of unique points;
Described trajectory analysis module provides the movement locus of target in real space in conjunction with the position of current goal set and historical goal set.
2, the tracker that has the binocular matching feature according to claim 1, it is characterized in that, described load module is responsible for gathering the digital picture of binocular camera shooting system, described digital picture is that digital camera and digital scanner image that can obtain and Digital Video provide the frame in the sequence image, each pixel value of photographic images is deposited in the internal storage location of region of memory correspondence in order, if input picture is a coloured image, then coloured image will be divided into R, G, three passages of B are preserved respectively.
3, the tracker that has the binocular matching feature according to claim 1, it is characterized in that, the eigenwert of the matrix that each neighborhood of pixels during described characteristic extracting module is calculated input picture is formed, when surpassing the threshold value that pre-sets, then this point is considered to the unique point in the image.
4, the tracker that has the binocular matching feature according to claim 1, it is characterized in that, described disparity estimation module is sought corresponding same point to the unique point that extracts in another input figure, the coordinate position of matching characteristic o'clock in two figure, in conjunction with parameters of pick-up head, its position in camera coordinate system can be estimated accurately, wherein match-on criterion is as contrast differences distance between the matrix of central point by NCC algorithm computation unique point, by the difference of position o'clock in two images in the real world is parallax, by obtaining parallax, demarcate the inside that obtains camera in advance in conjunction with two cameras, external parameter, with unique point x in image, y coordinate, parallax is converted to the coordinate figure under the camera coordinates.
5, the tracker that has the binocular matching feature according to claim 1, it is characterized in that, described world coordinate system computing module is after the unique point of extract handling has obtained coordinate under the camera coordinates system, with the camera coordinates of unique point be coordinate again in conjunction with the relation of the world coordinate system of demarcating in advance with camera coordinates system, it is to project in the world coordinate system that unique point is had camera coordinates again.
6, the tracker that has the binocular matching feature according to claim 1, it is characterized in that, the unique point that described target cluster module is will be in world coordinate system all aggregates into several set according to its height and position, the actual position of target in the space of these set correspondences.
7, the tracker that has the binocular matching feature according to claim 1, it is characterized in that, described trajectory analysis module, be meant: target is after the volume coordinate of every frame is expressed with set, and these discrete location points are determined the real motion track of whole target in the space by model.
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CN103083089B (en) * 2012-12-27 2014-11-12 广东圣洋信息科技实业有限公司 Virtual scale method and system of digital stereo-micrography system
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CN103337076A (en) * 2013-06-26 2013-10-02 深圳市智美达科技有限公司 Method and device for determining appearing range of video monitoring targets
CN103337076B (en) * 2013-06-26 2016-09-21 深圳市智美达科技股份有限公司 There is range determining method and device in video monitor object
CN103595916A (en) * 2013-11-11 2014-02-19 南京邮电大学 Double-camera target tracking system and implementation method thereof
CN104754733B (en) * 2013-12-31 2019-03-05 南京理工大学 Dynamic wireless network control system node location prediction technique
CN104754733A (en) * 2013-12-31 2015-07-01 南京理工大学 Node position prediction method of dynamic wireless network control system
CN103826071A (en) * 2014-03-11 2014-05-28 深圳市中安视科技有限公司 Three-dimensional camera shooting method for three-dimensional identification and continuous tracking
CN104915965A (en) * 2014-03-14 2015-09-16 华为技术有限公司 Camera tracking method and device
WO2015135323A1 (en) * 2014-03-14 2015-09-17 华为技术有限公司 Camera tracking method and device
CN104317391A (en) * 2014-09-24 2015-01-28 华中科技大学 Stereoscopic vision-based three-dimensional palm posture recognition interactive method and system
CN104317391B (en) * 2014-09-24 2017-10-03 华中科技大学 A kind of three-dimensional palm gesture recognition exchange method and system based on stereoscopic vision
CN104408718A (en) * 2014-11-24 2015-03-11 中国科学院自动化研究所 Gait data processing method based on binocular vision measuring
CN104408718B (en) * 2014-11-24 2017-06-30 中国科学院自动化研究所 A kind of gait data processing method based on Binocular vision photogrammetry
CN105898265A (en) * 2014-12-18 2016-08-24 陆婷 Novel stereo video-based human body tracking method
CN104539909A (en) * 2015-01-15 2015-04-22 安徽大学 Video monitoring method and video monitoring server
CN104820434A (en) * 2015-03-24 2015-08-05 南京航空航天大学 Velocity measuring method of ground motion object by use of unmanned plane
CN105160649A (en) * 2015-06-30 2015-12-16 上海交通大学 Multi-target tracking method and system based on kernel function unsupervised clustering
CN105072312A (en) * 2015-07-23 2015-11-18 柳州正高科技有限公司 Method for predicting image moving direction in dynamic video
CN106375654A (en) * 2015-07-23 2017-02-01 韩华泰科株式会社 Apparatus and method for controlling network camera
CN106375654B (en) * 2015-07-23 2020-09-01 韩华泰科株式会社 Apparatus and method for controlling web camera
CN105930766A (en) * 2016-03-31 2016-09-07 深圳奥比中光科技有限公司 Unmanned plane
CN105979203A (en) * 2016-04-29 2016-09-28 中国石油大学(北京) Multi-camera cooperative monitoring method and device
CN106657600A (en) * 2016-10-31 2017-05-10 维沃移动通信有限公司 Image processing method and mobile terminal
CN106657600B (en) * 2016-10-31 2019-10-15 维沃移动通信有限公司 A kind of image processing method and mobile terminal
CN106907988A (en) * 2017-02-27 2017-06-30 北京工业大学 The micro- visual modeling method of basic data matrix
CN106907988B (en) * 2017-02-27 2019-03-22 北京工业大学 The micro- visual modeling method of basic data matrix
CN110462684B (en) * 2017-04-10 2023-08-01 赫尔实验室有限公司 System, computer readable medium and method for implicit prediction of object movement
CN110462684A (en) * 2017-04-10 2019-11-15 赫尔实验室有限公司 Utilize the system of the movement of self-encoding encoder prediction object of interest
CN107292916A (en) * 2017-08-08 2017-10-24 阔地教育科技有限公司 Target association method, storage device, straight recorded broadcast interactive terminal
CN107292916B (en) * 2017-08-08 2020-10-27 阔地教育科技有限公司 Target association method, storage device and direct recording and broadcasting interactive terminal
CN107707821A (en) * 2017-09-30 2018-02-16 努比亚技术有限公司 Modeling method and device, bearing calibration, terminal, the storage medium of distortion parameter
CN107707821B (en) * 2017-09-30 2020-11-06 努比亚技术有限公司 Distortion parameter modeling method and device, correction method, terminal and storage medium
CN107958461A (en) * 2017-11-14 2018-04-24 中国航空工业集团公司西安飞机设计研究所 A kind of carrier aircraft method for tracking target based on binocular vision
CN108109176A (en) * 2017-12-29 2018-06-01 北京进化者机器人科技有限公司 Articles detecting localization method, device and robot
CN108257146A (en) * 2018-01-15 2018-07-06 新疆大学 Movement locus display methods and device
CN108257146B (en) * 2018-01-15 2020-08-18 新疆大学 Motion trail display method and device
WO2020020160A1 (en) * 2018-07-25 2020-01-30 北京市商汤科技开发有限公司 Image parallax estimation
CN108877269A (en) * 2018-08-20 2018-11-23 清华大学 A kind of detection of intersection vehicle-state and V2X broadcasting method
CN108877269B (en) * 2018-08-20 2020-10-27 清华大学 Intersection vehicle state detection and V2X broadcasting method
CN110956644B (en) * 2018-09-27 2023-10-10 杭州海康威视数字技术股份有限公司 Motion trail determination method and system
CN110956644A (en) * 2018-09-27 2020-04-03 杭州海康威视数字技术股份有限公司 Motion trail determination method and system
CN111179305A (en) * 2018-11-13 2020-05-19 晶睿通讯股份有限公司 Object position estimation method and object position estimation device
CN111179305B (en) * 2018-11-13 2023-11-14 晶睿通讯股份有限公司 Object position estimation method and object position estimation device thereof
CN111369622A (en) * 2018-12-25 2020-07-03 中国电子科技集团公司第十五研究所 Method, device and system for acquiring camera world coordinate position by virtual and real superposition application
CN111369622B (en) * 2018-12-25 2023-12-08 中国电子科技集团公司第十五研究所 Method, device and system for acquiring world coordinate position of camera by virtual-real superposition application
CN111723830B (en) * 2019-03-20 2023-08-29 杭州海康威视数字技术股份有限公司 Image mapping method, device and equipment and storage medium
CN111723830A (en) * 2019-03-20 2020-09-29 杭州海康威视数字技术股份有限公司 Image mapping method, device and equipment and storage medium
CN110428449B (en) * 2019-07-31 2023-08-04 腾讯科技(深圳)有限公司 Target detection tracking method, device, equipment and storage medium
CN110428449A (en) * 2019-07-31 2019-11-08 腾讯科技(深圳)有限公司 Target detection tracking method, device, equipment and storage medium
CN111199556B (en) * 2019-12-31 2023-07-04 同济大学 Indoor pedestrian detection and tracking method based on camera
CN111199556A (en) * 2019-12-31 2020-05-26 同济大学 Indoor pedestrian detection and tracking method based on camera
CN111698467A (en) * 2020-05-08 2020-09-22 北京中广上洋科技股份有限公司 Intelligent tracking method and system based on multiple cameras
CN111698467B (en) * 2020-05-08 2022-05-06 北京中广上洋科技股份有限公司 Intelligent tracking method and system based on multiple cameras
CN111918023A (en) * 2020-06-29 2020-11-10 北京大学 Monitoring target tracking method and device
CN111918023B (en) * 2020-06-29 2021-10-22 北京大学 Monitoring target tracking method and device
CN112037159A (en) * 2020-07-29 2020-12-04 长安大学 Cross-camera road space fusion and vehicle target detection tracking method and system
CN112037159B (en) * 2020-07-29 2023-06-23 中天智控科技控股股份有限公司 Cross-camera road space fusion and vehicle target detection tracking method and system
CN111899282A (en) * 2020-07-30 2020-11-06 平安科技(深圳)有限公司 Pedestrian trajectory tracking method and device based on binocular camera calibration
CN112419361A (en) * 2020-11-20 2021-02-26 中国科学院上海微系统与信息技术研究所 Target tracking method and bionic vision device
CN112419361B (en) * 2020-11-20 2024-04-05 中国科学院上海微系统与信息技术研究所 Target tracking method and bionic vision device
CN112991401A (en) * 2021-01-20 2021-06-18 深兰科技(上海)有限公司 Vehicle running track tracking method and device, electronic equipment and storage medium
CN113311873A (en) * 2021-05-07 2021-08-27 中国科学院沈阳自动化研究所 Unmanned aerial vehicle servo tracking method based on vision
CN113313759B (en) * 2021-05-31 2023-02-14 华南理工大学 Welding spot positioning method, system, device, equipment and medium based on binocular vision
CN113313759A (en) * 2021-05-31 2021-08-27 华南理工大学 Welding spot positioning method, system, device, equipment and medium based on binocular vision
CN115713565A (en) * 2022-12-16 2023-02-24 盐城睿算电子科技有限公司 Target positioning method for binocular servo camera

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