CN108458691A - A kind of collision checking method and equipment - Google Patents
A kind of collision checking method and equipment Download PDFInfo
- Publication number
- CN108458691A CN108458691A CN201810106578.3A CN201810106578A CN108458691A CN 108458691 A CN108458691 A CN 108458691A CN 201810106578 A CN201810106578 A CN 201810106578A CN 108458691 A CN108458691 A CN 108458691A
- Authority
- CN
- China
- Prior art keywords
- moving object
- collision
- video
- testing result
- collision factor
- 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
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
- G01C11/04—Interpretation of pictures
Landscapes
- Engineering & Computer Science (AREA)
- Multimedia (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Image Analysis (AREA)
Abstract
This application provides a kind of collision checking method and equipment, in scheme provided by the present application, after obtaining video to be detected, the moving object for including in video is detected based on depth learning technology, as the first testing result, the background and foreground in technology separation video are wiped out based on background simultaneously, and the moving object for including in video is detected in the foreground, as the second testing result, then integrated treatment is carried out according to the first testing result and the second testing result, determine the moving object for including in video and its corresponding collision factor information, and then according to the collision factor information of moving object determine moving object between whether collide.Since depth learning technology is utilized in the first testing result, accuracy of detection is higher, and it is supplemented with second the first testing result of testing result pair, further improve the precision of testing result, so that the moving object and its corresponding collision factor information that determine with this are more accurate, the precision of collision detection is which thereby enhanced.
Description
Technical field
This application involves information technology field more particularly to a kind of collision checking method and equipment.
Background technology
In recent years, with the rapid development of economy, urban infrastructure construction and vehicles number all achieve prominent fly
The development pushed ahead vigorously, while bringing great convenience, congested in traffic, traffic accident occurrence frequency is consequently increased,
Influence the various aspects such as production, the life of people.One studied just as Modern Traffic of traffic accident is extremely important
Research field.Traffic video research based on video image technology becomes an importance for solving traffic accident.Currently, base
In the scheme that video image technology is detected traffic accident collision accident, accuracy of detection is limited, so as to cause detection
As a result accuracy is not high, and also has no way of judging for the specific situation of traffic accident.
Apply for content
The purpose of the application is to provide a kind of collision detection scheme, to solve the problems, such as that accuracy of detection is not high.
To achieve the above object, this application provides a kind of collision checking method, this method includes：
Obtain video to be detected；
The moving object for including in the video is detected based on deep learning technology, as the first testing result；
Background and foreground in the video are detached based on the background technology of wiping out, and detects the video in the foreground
In include moving object, as the second testing result；
Integrated treatment is carried out according to first testing result and second testing result, determines in the video and includes
Moving object and its corresponding collision factor information；
Whether collide between determining the moving object according to the collision factor information of the moving object.
Another aspect based on the application additionally provides a kind of crash detection device, which includes：
Input unit, for obtaining video to be detected；
First detection device, for detecting the moving object for including in the video based on deep learning technology, as
One testing result；
Second detection device, for detaching background and foreground in the video based on the background technology of wiping out, and described
The moving object for including in the video is detected in foreground, as the second testing result；
Fusing device is tracked, for carrying out integrated treatment according to first testing result and second testing result,
Determine the moving object for including in the video and its corresponding collision factor information；
Judgment means, for whether being sent out between determining the moving object according to the collision factor information of the moving object
Raw collision.
In addition, present invention also provides a kind of crash detection device, which includes：
Processor；And
One or more machine readable medias of machine readable instructions are stored with, when the processor execution machine can
When reading instruction so that the equipment executes such as method described in any item of the claim 1 to 8.
In scheme provided by the present application, after obtaining video to be detected, based on being regarded described in deep learning technology detection
The moving object for including in frequency detaches the background in the video as the first testing result, while based on the background technology of wiping out
And foreground, and the moving object for including in the video is detected in the foreground, as the second testing result, then according to institute
State the first testing result and second testing result and carry out integrated treatment, determine the moving object for including in the video and its
Corresponding collision factor information, so according to the collision factor information of the moving object determine between the moving object whether
It collides.Since deep learning technology is utilized in the first testing result, accuracy of detection is higher, and with the second testing result pair
First testing result is supplemented, and the precision of testing result is further improved so that the moving object that is determined with this and its right
The collision factor information answered is more accurate, which thereby enhances the precision of collision detection.
Description of the drawings
By reading a detailed description of non-restrictive embodiments in the light of the attached drawings below, the application's is other
Feature, objects and advantages will become more apparent upon：
Fig. 1 is a kind of process chart of collision checking method provided by the embodiments of the present application；
Fig. 2 is the signal being detected to collision class traffic accident using collision checking method provided by the embodiments of the present application
Figure；
Fig. 3 is a kind of schematic diagram of crash detection device provided by the embodiments of the present application；
Fig. 4 is the schematic diagram of another crash detection device provided by the embodiments of the present application；
Same or analogous reference numeral represents same or analogous component in attached drawing.
Specific implementation mode
The application is described in further detail below in conjunction with the accompanying drawings.
In a typical configuration of this application, terminal, the equipment of service network include one or more processors
(CPU), input/output interface, network interface and memory.
Memory may include computer-readable medium in volatile memory, random access memory (RAM) and/or
The forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flash RAM).Memory is computer-readable medium
Example.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media, can be by any side
Method or technology realize information storage.Information can be computer-readable instruction, data structure, program device or other number
According to.The example of the storage medium of computer includes, but are not limited to phase transition internal memory (PRAM), static RAM
(SRAM), dynamic random access memory (DRAM), other kinds of random access memory (RAM), read-only memory
(ROM), electrically erasable programmable read-only memory (EEPROM), fast flash memory bank or other memory techniques, CD-ROM (CD-
ROM), digital versatile disc (DVD) or other optical storages, magnetic tape cassette, magnetic tape disk storage or other magnetic storages
Equipment or any other non-transmission medium can be used for storage and can be accessed by a computing device information.
The embodiment of the present application provides a kind of collision checking method, and this method can examine the moving object in video
It surveys to judge whether to collide, the executive agent of this method can be user equipment, the network equipment or user equipment and network
Equipment is integrated constituted equipment by network, or can also be the application program for running on above equipment.The user
Equipment includes but not limited to all kinds of terminal devices such as computer, mobile phone, tablet computer；The network equipment includes but not limited to such as
The realizations such as network host, single network server, multiple network server collection or the set of computers based on cloud computing.Here,
Cloud is made of a large amount of hosts or network server for being based on cloud computing (Cloud Computing), wherein cloud computing is distributed
One kind of calculating, a virtual machine being made of the computer collection of a group loose couplings.
Fig. 1 shows a kind of process chart of collision checking method in the embodiment of the present application, including following processing step：
Step S101 obtains video to be detected.Video to be detected refers to the multiframe picture arranged according to sequential, can be with
It shoots to obtain by all kinds of photographic devices.For example, when this programme is applied to the collision accident detection in traffic accident, these are waited for
The video of detection can be shot to obtain by the monitoring camera being installed on road.
Step S102 detects the moving object for including in the video based on deep learning technology, as the first detection knot
Fruit.Before using deep learning technology detection, need to be trained the model being detected by training set.Training set needs
When being made of the video for belonging to similar field, such as need to detect the collision accident in traffic accident, due to traffic accident
Involved in moving object generally comprise all kinds of motor vehicles, pedestrian, bicycle etc., therefore the video in training set be also required to include
These moving objects, and the training sample in training set is more, and the accuracy of obtained model when detecting is trained also to get over
It is high.
Since deep learning technology is other than identifying the moving object for including in video, moving object can also be identified
Entity type.Classification for entity type can be set according to the field of the application of this programme, for example, being applied to
When collision accident in row traffic accident detects, entity type can be divided into motor vehicle, non-motor vehicle, pedestrian etc., can also
It is further subdivided into car, bus, bicycle, motorcycle, pedestrian, truck, oil truck etc..As a result, based on depth
When habit technology detects the moving object for including in the video, the entity type of the moving object can also while be determined.
Step S103 detaches background and foreground in the video based on the background technology of wiping out, and is examined in the foreground
The moving object for including in the video is surveyed, as the second testing result.The step can be synchronous with the processing in step S102
It executes, i.e., the mode of moving object is detected at two kinds can synchronize progress, and obtain testing result respectively.The embodiment of the present application
Employed in the background technology of wiping out arbitrary existing mature technology may be used, such as carried out based on mixture Gaussian background model
The separation of foreground and background, moving object of the mobile target as the second testing result in extraction foreground.
Since when progress background wipes out technology, the stability of background image can cause a fixing to the precision of detection
It rings, such as background frame is fixed inconvenient, then the differentiation precision of its background and foreground is higher.Therefore, in the embodiment of the present application
It can obtain by the video to be detected for the photographic device shooting being fixedly installed so that the background of video to be detected is relatively fixed,
To improve the accuracy of detection that background wipes out technology.
Step S104 carries out integrated treatment according to first testing result and second testing result, determine described in
The moving object for including in video and its corresponding collision factor information.When carrying out integrated treatment, two different sides are utilized
The purpose that formula is respectively detected the moving object in video is the inspection for wiping out technology to deep learning technology using background
It surveys result to be supplemented, the case where avoiding the detection of deep learning technology there are missing inspections, to influence the accuracy of subsequent processing.
For example, moving object A is not detected in the first testing result, and moving object A is detected in the second testing result, be based on institute
It states the second testing result to supplement first testing result, using moving object A as including in finally determining video
One of moving object.
Whether the corresponding collision factor information of these moving objects refers to being touched between being determined for moving object
The information hit, such as can be movement velocity, the direction of motion, running orbit, contour of object etc..When detecting, contour of object can
It is indicated in the form of using detection block, (x, y, w, h) four vector may be used to indicate, wherein x, y are to detect object
Body frame top left co-ordinate, w are the width of detection block, the height that h is detection block.These collision factor informations can be in video
The moving object detected carries out the mode of Kalman filter tracking to obtain.
In the embodiment of the present application, first testing result is supplemented based on second testing result, and carries out
When Kalman filter tracking, can first respectively to first testing result and the second testing result carry out Kalman filtering with
Track determines the collision factor observation of the first moving object collection in the video frame and collision factor predicted value and the second moving object
The collision factor observation in the video frame and collision factor predicted value of body collection.Wherein, the first moving object collection is the first inspection
The set of moving object in result is surveyed, the second moving object collection is the set of moving object in the second testing result, collides factor
Observation refers to the actually detected value arrived in each video frame, and the factor predicted value of collision refers to based on video frame pair before
The value that current video frame is estimated, be then based on predicted value and observation, and is finally determined in conjunction with the noise of the two
It collides factor information.
For colliding the speed in factor information, to the speed of moving object A when obtaining kth frame, then root first
Velocity amplitude when according to k-1 frames predicts rate predictions when k frames, it is assumed that thinks that moving object is at the uniform velocity based on data before
Movement, then velocity amplitude when can be using k-1 frames is as rate predictions when k frames, it is assumed that for 23km/h, while the noise of the value
Deviation is 5km/h (white Gaussian noise may be used).And the speed that the practical measurement of image based on kth frame and before obtains
Value is the speed observation of moving object A, it is assumed that is 25km/h, noise bias value is 4km/h.
Since when carrying out Kalman filter tracking, observation is to need to obtain by actually detected, and predicted value is logical
The data for crossing preamble video frame are predicted to obtain, for the frame it is possible that only pre- if missing inspection occurs in a certain frame
Measured value, and the case where without observation.Thus, it is possible to the first moving object collection and the second moving object collection and corresponding collision
Factor observation and collision factor predicted value are matched, if some moving object A that the first moving object is concentrated only is got
Collision factor predicted value, and collisionless factor observation, but due to the difference of detection mode, the second moving object concentration includes
The collision factor predicted value of moving object A can then be used as supplement, obtain the collision factor predicted value of moving object A and touch
Hit factor predicted value.
Further it is also possible to which a certain moving object A is not detected in deep learning technology, then the first moving object concentration is not wrapped
A containing moving object can not get the collision factor observation and collision factor predicted value of moving object A.And background is wiped out
Technology detects moving object A, then the second moving object concentration contains moving object A, and also gets moving object
The collision factor observation and collision factor predicted value of body A, it is possible thereby to the number of the moving object A concentrated with the second moving object
It is supplemented according to overall data, so as to prevent missing inspection, improves accuracy.
No matter when above-mentioned which kind of situation, to the first moving object collection and the second moving object in the embodiment of the present application
When collection and corresponding collision factor observation and collision factor predicted value are matched, Hungary matching algorithm may be used,
The data that two kinds of detection modes obtain are matched to the greatest extent.
The moving object for including in the video and its corresponding collision factor observation and collision are being determined by matching
It, can be according to Kalman filtering algorithm, based on the corresponding collision of moving object for including in the video after factor predicted value
Factor observation and collision factor predicted value, calculate the collision factor information of the moving object, such as in conjunction with speed observation
Velocity amplitudes of the moving object A in k frames is finally calculated with the covariance of rate predictions.
Whether step S105 touches between determining the moving object according to the collision factor information of the moving object
It hits.Since in actual scene, all kinds of moving objects will appear a series of phenomenons when colliding, by these phenomenon regularization,
It is indicated with specifically colliding factor information, then it can be as the standard for determining whether to collide.
For collision in traffic accident, if in motion process, the overlapping of moving object segmentation frame is more than certain threshold value,
Then think to collide, needs whether concern wherein has the case where detection block disappearance (can not detect) at this time, if there is
One side or multi-party disappearance, then can regard as colliding.Also such as, both sides' movement locus can be further paid close attention to, if movement side
It then may not be collision in fact to parallel, and both be only to pass through, such as the vehicle of traveling have passed through the pedestrian to walk in the same direction
By the side of, and the pedestrian has been sheltered from.But if the angle of the direction of motion is larger, illustrate there is apparent relative motion, then illustrates very
It has been likely to occur collision.In addition, after general collision occurs, the moving objects such as other vehicles of surrounding, pedestrian can be collided
The influence of accident, it is even motionless to slow down within a certain period of time, therefore can make using such phenomenon as a part for rule
To determine whether the foundation to collide.
When traffic accident occurs, when colliding between different traffic main bodys, the traffic of different severity can be caused
Accident.For example, having the traffic accident that pedestrian participates in often someone will member's injury, its severity generally can all be more than between vehicle
Accident, and have the oversize vehicles such as oil truck, bus, truck participate in traffic accident also tend to cause great safety hidden
Suffer from, and if it is the collision between car, and speed is little, then it is believed that not being serious accident.As a result, according to
The collision factor information of moving object determines whether collide between the moving object after, however, it is determined that collide, also
The severity of the secondary collision can be determined according to the entity type and collision factor information of the moving object.
When determining the severity of the secondary collision according to the entity type and collision factor information of the moving object,
The associated video frame for being related to the secondary accident can be extracted in the video, then according only to described in the associated video frame
The entity type of movement entity and collision factor information determine the severity of the secondary collision.For example, the length of one section of video
Degree is 300 frames, wherein be related to impact generation process is the 80th to 120 frame, can only extract the 80th to 120 frame work at this time
For associated video frame, the judgement for carrying out crash severity.
In addition, in the collision checking method that some embodiments of the application provide, it is also based on deep learning technology detection
Additional Event information in the associated video frame, these additional Event informations can be set according to actual application scenarios
And training, such as in the collision detection of traffic accident, additional Event information can be flue dust, fire behavior etc., if collision causes
On fire, the case where smoldering, its severity can be larger, therefore can believe further combined with additional events when determining severity
Breath, i.e., according to the collision factor information of the movement entity in the entity type of the moving object, the associated video frame
And additional Event information, determine the severity of the secondary collision.
Include such as Fig. 2 shows the scheme being detected to collision class traffic accident using the scheme of the application, the program
Under processing step：
S201 is chronologically inputted per frame video.
S202 carries out the detection of moving object using deep learning technology, can identify the specific reality of these moving objects
Body type, including car, bus, bicycle, motorcycle, pedestrian and truck, oil truck etc..
S203 is carrying out deep learning detection simultaneously, and wiping out technology using background carries out foreground and background separation, before identification
Moving object in scape.
S204 detects that moving object carries out Kalman filter tracking to each in S202, while to being detached in S203
The moving object of the foreground gone out carries out Kalman filter tracking.
S205, the tracking object that the moving object and background subtraction that deep learning detects detect pass through Hungary Algorithm
It is matched, determines final moving object and collision factor information.
S206 is sentenced using the collision such as the speed of moving object, direction, running orbit, detection block factor information into line discipline
It is fixed, meet preset condition and is considered to be collided.
S207 carries out collision accident deciding degree, determines severity for the associated video frame that judgement collides,
Such as whether thering is pedestrian to be involved in, whether be public transport or truck etc. have the vehicle of major safety risks to be involved in.
S208 carries out the detection of the additional Event informations such as fire behavior, flue dust, to assist to define severity of injuries.
Based on same inventive concept, crash detection device is additionally provided in the embodiment of the present application, the corresponding side of the equipment
Method is the method in previous embodiment, and its principle solved the problems, such as is similar to this method.
Crash detection device provided by the embodiments of the present application can be detected the moving object in video is to judge
No to collide, the executive agent of this method can be that user equipment, the network equipment or user equipment and the network equipment pass through net
Network is integrated constituted equipment, or can also be the application program for running on above equipment.The user equipment include but
It is not limited to all kinds of terminal devices such as computer, mobile phone, tablet computer；The network equipment include but not limited to as network host,
The realizations such as single network server, multiple network server collection or the set of computers based on cloud computing.Here, cloud is by being based on cloud
The a large amount of hosts or network server for calculating (Cloud Computing) are constituted, wherein cloud computing is the one of Distributed Calculation
Kind, a virtual machine being made of the computer collection of a group loose couplings.
Fig. 3 shows that a kind of structural schematic diagram of crash detection device in the embodiment of the present application, the equipment include input
Device 310, the first detection device 320, second detection device 330, tracking fusing device 340 and judgment means 350.Wherein, defeated
Enter device 310 for obtaining video to be detected.Video to be detected refers to the multiframe picture arranged according to sequential, can be by each
Class photographic device shoots to obtain.For example, when this programme is applied to the collision accident detection in traffic accident, these are to be detected
Video can be shot to obtain by the monitoring camera that is installed on road.
First detection device 320 is used to detect the moving object for including in the video based on deep learning technology, as
First testing result.Before using deep learning technology detection, need to instruct the model being detected by training set
Practice.Training set needs be made of the video for belonging to similar field, such as need in traffic accident collision accident detection when, by
All kinds of motor vehicles, pedestrian, bicycle etc., therefore the video in training set are generally comprised in the moving object involved in traffic accident
It is also required to include these moving objects, and the training sample in training set is more, trains obtained model when detecting
Accuracy is also higher.
Deep learning technology can also identify the reality of moving object other than identifying the moving object for including in video
Body type.Classification for entity type can be set according to the field of the application of this programme, for example, being handed over applied to row
When collision accident in interpreter's event detects, entity type can be divided into motor vehicle, non-motor vehicle, pedestrian etc., can also be into one
Step is subdivided into car, bus, bicycle, motorcycle, pedestrian, truck, oil truck etc..As a result, based on deep learning skill
When art detects the moving object for including in the video, the first detection device can also determine the entity of the moving object simultaneously
Type.
Second detection device 330 is used to detach background and foreground in the video based on the background technology of wiping out, and in institute
The moving object for being detected in foreground and including in the video is stated, as the second testing result.Processing in second detection device can
It executing with synchronous with the processing in the first detection device, i.e., the mode of moving object is detected at two kinds can synchronize progress, and
Testing result is obtained respectively.Arbitrary existing ripe skill may be used in the background technology of wiping out employed in the embodiment of the present application
Art, such as the separation of foreground and background is carried out based on mixture Gaussian background model, the mobile target in extraction foreground is as second
The moving object of testing result.
Since when progress background wipes out technology, the stability of background image can cause a fixing to the precision of detection
It rings, such as background frame is fixed inconvenient, then the differentiation precision of its background and foreground is higher.Therefore, in the embodiment of the present application
Input unit can be obtained by the video to be detected for the photographic device shooting being fixedly installed so that the background phase of video to be detected
To fixation, to improve the accuracy of detection that background wipes out technology.
Fusing device 340 is tracked to be used to carry out General Office according to first testing result and second testing result
Reason, determines the moving object for including in the video and its corresponding collision factor information.When carrying out integrated treatment, two are utilized
The purpose that the different mode of kind is respectively detected the moving object in video is to wipe out technology to depth using background
The testing result of habit technology is supplemented, the case where avoiding the detection of deep learning technology there are missing inspections, to influence follow-up place
The accuracy of reason.For example, moving object A is not detected in the first testing result, and movement is detected in the second testing result
Object A supplements first testing result based on second testing result, is determined using moving object A as final
Video in include one of moving object.
Whether the corresponding collision factor information of these moving objects refers to being touched between being determined for moving object
The information hit, such as can be movement velocity, the direction of motion, running orbit, contour of object etc..When detecting, contour of object can
It is indicated in the form of using detection block, (x, y, w, h) four vector may be used to indicate, wherein x, y are to detect object
Body frame top left co-ordinate, w are the width of detection block, the height that h is detection block.These collision factor informations can be in video
The moving object detected carries out the mode of Kalman filter tracking to obtain.
In the embodiment of the present application, tracking fusing device based on second testing result to first testing result into
When going and supplement, and carrying out Kalman filter tracking, first first testing result and the second testing result can be carried out respectively
Kalman filter tracking, determine the collision factor observation of the first moving object collection in the video frame and collision factor predicted value with
And second moving object collection collision factor observation in the video frame and collision factor predicted value.Wherein, the first moving object
Body collection is the set of moving object in the first testing result, and the second moving object collection is the collection of moving object in the second testing result
It closes, collision factor observation refers to the actually detected value arrived in each video frame, and the factor predicted value of collision refers to being based on it
The value that preceding video frame estimates current video frame is then based on predicted value and observation, and combines making an uproar for the two
Sound come finally determine its collide factor information.
For colliding the speed in factor information, to the speed of moving object A when obtaining kth frame, then root first
Velocity amplitude when according to k-1 frames predicts rate predictions when k frames, it is assumed that thinks that moving object is at the uniform velocity based on data before
Movement, then velocity amplitude when can be using k-1 frames is as rate predictions when k frames, it is assumed that for 23km/h, while the noise of the value
Deviation is 5km/h (white Gaussian noise may be used).And the speed that the practical measurement of image based on kth frame and before obtains
Value is the speed observation of moving object A, it is assumed that is 25km/h, noise bias value is 4km/h.
Since when carrying out Kalman filter tracking, observation is to need to obtain by actually detected, and predicted value is logical
The data for crossing preamble video frame are predicted to obtain, for the frame it is possible that only pre- if missing inspection occurs in a certain frame
Measured value, and the case where without observation.Thus, it is possible to the first moving object collection and the second moving object collection and corresponding collision
Factor observation and collision factor predicted value are matched, if some moving object A that the first moving object is concentrated only is got
Collision factor predicted value, and collisionless factor observation, but due to the difference of detection mode, the second moving object concentration includes
The collision factor predicted value of moving object A can then be used as supplement, obtain the collision factor predicted value of moving object A and touch
Hit factor predicted value.
Further it is also possible to which a certain moving object A is not detected in deep learning technology, then the first moving object concentration is not wrapped
A containing moving object can not get the collision factor observation and collision factor predicted value of moving object A.And background is wiped out
Technology detects moving object A, then the second moving object concentration contains moving object A, and also gets moving object
The collision factor observation and collision factor predicted value of body A, it is possible thereby to the number of the moving object A concentrated with the second moving object
It is supplemented according to overall data, so as to prevent missing inspection, improves accuracy.
No matter when above-mentioned which kind of situation, in the embodiment of the present application tracking fusing device to the first moving object collection and
When second moving object collection and corresponding collision factor observation and collision factor predicted value are matched, breast tooth may be used
Sharp matching algorithm so that the data that two kinds of detection modes obtain can match to the greatest extent.
The moving object for including in the video and its corresponding collision factor observation and collision are being determined by matching
It, can be according to Kalman filtering algorithm, based on the corresponding collision of moving object for including in the video after factor predicted value
Factor observation and collision factor predicted value, calculate the collision factor information of the moving object, such as in conjunction with speed observation
Velocity amplitudes of the moving object A in k frames is finally calculated with the covariance of rate predictions.
Judgment means 350 be used for according to the collision factor information of the moving object determine between the moving object whether
It collides.Since in actual scene, all kinds of moving objects will appear a series of phenomenons when colliding, these phenomenons are advised
Then change, is indicated with specifically colliding factor information, then it can be as the standard for determining whether to collide.
For collision in traffic accident, if in motion process, the overlapping of moving object segmentation frame is more than certain threshold value,
Then think to collide, needs whether concern wherein has the case where detection block disappearance (can not detect) at this time, if there is
One side or multi-party disappearance, then can regard as colliding.Also such as, both sides' movement locus can be further paid close attention to, if movement side
It then may not be collision in fact to parallel, and both be only to pass through, such as the vehicle of traveling have passed through the pedestrian to walk in the same direction
By the side of, and the pedestrian has been sheltered from.But if the angle of the direction of motion is larger, illustrate there is apparent relative motion, then illustrates very
It has been likely to occur collision.In addition, after general collision occurs, the moving objects such as other vehicles of surrounding, pedestrian can be collided
The influence of accident, it is even motionless to slow down within a certain period of time, therefore can make using such phenomenon as a part for rule
To determine whether the foundation to collide.
When traffic accident occurs, when colliding between different traffic main bodys, the traffic of different severity can be caused
Accident.For example, having the traffic accident that pedestrian participates in often someone will member's injury, its severity generally can all be more than between vehicle
Accident, and have the oversize vehicles such as oil truck, bus, truck participate in traffic accident also tend to cause great safety hidden
Suffer from, and if it is the collision between car, and speed is little, then it is believed that not being serious accident.As a result, according to
The collision factor information of moving object determines whether collide between the moving object after, however, it is determined that collide, sentence
Disconnected device can also determine the severity of the secondary collision according to the entity type and collision factor information of the moving object.
When determining the severity of the secondary collision according to the entity type and collision factor information of the moving object,
Judgment means can extract the associated video frame for being related to the secondary accident in the video, then according only to the associated video frame
In the movement entity entity type and collision factor information determine the severity of the secondary collision.For example, one section
The length of video be 300 frames, wherein be related to impact generation process is the 80th to 120 frame, can only extract at this time the 80th to
120 frames are as associated video frame, the judgement for carrying out crash severity.
In addition, in the collision checking method that some embodiments of the application provide, the first detection device is also based on depth
Learning art detects the additional Event information in the associated video frame, these additional Event informations can be according to actual application
Scene is set and is trained, such as in the collision detection of traffic accident, and additional Event information can be flue dust, fire behavior etc.,
If collision causes on fire, the case where smoldering, its severity can be larger, therefore judgment means can be with when determining severity
Further combined with additional Event information, i.e., according to the fortune in the entity type of the moving object, the associated video frame
The collision factor information and additional Event information of dynamic entity, determine the severity of the secondary collision.
In conclusion in scheme provided by the present application, after obtaining video to be detected, examined based on deep learning technology
The moving object for including in the video is surveyed, as the first testing result, while technology is wiped out based on background and detaches the video
In background and foreground, and the moving object for including in the video is detected in the foreground, as the second testing result, so
Integrated treatment is carried out according to first testing result and second testing result afterwards, determines the movement for including in the video
Object and its corresponding collision factor information, and then the moving object is determined according to the collision factor information of the moving object
Between whether collide.Since deep learning technology is utilized in the first testing result, accuracy of detection is higher, and with the second inspection
It surveys the first testing result of result pair to supplement, further improves the precision of testing result so that the moving object determined with this
Body and its corresponding collision factor information are more accurate, which thereby enhance the precision of collision detection.
In addition, the entity type for the moving object that this programme can also clearly be collided using deep learning technology, by
This can further judge the severity of collision accident, and information definitely is provided for collision detection.
In addition, the part of the application can be applied to computer program product, such as computer program instructions, when its quilt
When computer executes, by the operation of the computer, it can call or provide according to the present processes and/or technical solution.
And the program instruction of the present processes is called, it is possibly stored in fixed or moveable recording medium, and/or pass through
Broadcast or the data flow in other signal loaded mediums and be transmitted, and/or be stored in the calculating run according to program instruction
In the working storage of machine equipment.Here, including an equipment as shown in Figure 4 according to one embodiment of the application, this sets
Standby includes the one or more machine readable medias 410 for being stored with machine readable instructions and the place for executing machine readable instructions
Manage device 420, wherein when the machine readable instructions are executed by the processor so that the equipment is executed based on aforementioned according to this
The method and/or technology scheme of multiple embodiments of application.
It should be noted that the application can be carried out in the assembly of software and/or software and hardware, for example, can adopt
With application-specific integrated circuit (ASIC), general purpose computer or any other realized similar to hardware device.In one embodiment
In, the software program of the application can be executed by processor to realize above step or function.Similarly, the software of the application
Program (including relevant data structure) can be stored in computer readable recording medium storing program for performing, for example, RAM memory, magnetic or
CD-ROM driver or floppy disc and similar devices.In addition, hardware can be used to realize in some steps or function of the application, for example,
Coordinate to execute the circuit of each step or function as with processor.
It is obvious to a person skilled in the art that the application is not limited to the details of above-mentioned exemplary embodiment, Er Qie
In the case of without departing substantially from spirit herein or essential characteristic, the application can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and scope of the present application is by appended power
Profit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent requirements of the claims
Variation is included in the application.Any reference signs in the claims should not be construed as limiting the involved claims.This
Outside, it is clear that one word of " comprising " is not excluded for other units or step, and odd number is not excluded for plural number.That is stated in device claim is multiple
Unit or device can also be realized by a unit or device by software or hardware.The first, the second equal words are used for table
Show title, and does not represent any particular order.
Claims (17)
1. a kind of collision checking method, wherein this method includes：
Obtain video to be detected；
The moving object for including in the video is detected based on deep learning technology, as the first testing result；
Background and foreground in the video are detached based on the background technology of wiping out, and detects in the foreground and is wrapped in the video
The moving object contained, as the second testing result；
Integrated treatment is carried out according to first testing result and second testing result, determines the fortune for including in the video
Animal body and its corresponding collision factor information；
Whether collide between determining the moving object according to the collision factor information of the moving object.
2. according to the method described in claim 1, wherein, being carried out according to first testing result and second testing result
Integrated treatment determines the moving object for including in the video and its corresponding collision factor information, including：
First testing result is supplemented based on second testing result, and carries out Kalman filter tracking, is determined
The moving object for including in the video and its corresponding collision factor information.
3. according to the method described in claim 2, wherein, being carried out to first testing result based on second testing result
Supplement, and Kalman filter tracking is carried out, including：
Kalman filter tracking is carried out to first testing result and the second testing result respectively, determines the first moving object collection
The touching in the video frame of collision factor observation in the video frame and collision factor predicted value and the second moving object collection
Hit factor observation and collision factor predicted value, wherein the first moving object collection is moving object in the first testing result
Set, the second moving object collection be the second testing result in moving object set；
It is pre- to the first moving object collection and the second moving object collection and corresponding collision factor observation and collision factor
Measured value is matched, and determines that the moving object for including in the video and its corresponding collision factor observation and collision factor are pre-
Measured value；
According to Kalman filtering algorithm, based on the corresponding collision factor observation of moving object for including in the video and collision
Factor predicted value calculates the collision factor information of the moving object.
4. according to the method described in claim 1, wherein, the moving object for including in the video is detected based on deep learning technology
When body, further include：
Determine the entity type of the moving object.
5. according to the method described in claim 4, wherein, the movement is determined according to the collision factor information of the moving object
After whether colliding between object, further include：
If it is determined that colliding, the tight of the secondary collision is determined according to the entity type of the moving object and collision factor information
Weight degree.
6. according to the method described in claim 5, wherein, however, it is determined that collide, according to the entity type of the moving object
And collision factor information determines the severity of the secondary collision, including：
If it is determined that colliding, the associated video frame for being related to the secondary accident is extracted in the video；
Believed according to the collision factor of the movement entity in the entity type of the moving object and the associated video frame
Breath determines the severity of the secondary collision.
7. according to the method described in claim 6, wherein, this method further includes：
The additional Event information in the associated video frame is detected based on deep learning technology；
Believed according to the collision factor of the movement entity in the entity type of the moving object and the associated video frame
Breath determines the severity of the secondary collision, including：
According to the collision factor information of the movement entity in the entity type of the moving object, the associated video frame with
And additional Event information, determine the severity of the secondary collision.
8. according to the method described in claim 1, wherein, obtain video to be detected, including：
It obtains by the video to be detected for the photographic device shooting being fixedly installed.
9. a kind of crash detection device, wherein the equipment includes：
Input unit, for obtaining video to be detected；
First detection device, for detecting the moving object for including in the video based on deep learning technology, as the first inspection
Survey result；
Second detection device, for detaching background and foreground in the video based on the background technology of wiping out, and in the foreground
The moving object for including in the middle detection video, as the second testing result；
Fusing device is tracked, for carrying out integrated treatment according to first testing result and second testing result, is determined
The moving object for including in the video and its corresponding collision factor information；
Judgment means, for whether being touched between determining the moving object according to the collision factor information of the moving object
It hits.
10. equipment according to claim 9, wherein the tracking fusing device, for being based on second testing result
First testing result is supplemented, and carries out Kalman filter tracking, determines the moving object for including in the video
And its corresponding collision factor information.
11. equipment according to claim 10, wherein the tracking fusing device, for respectively to first detection
As a result Kalman filter tracking is carried out with the second testing result, determines that the collision factor of the first moving object collection in the video frame is seen
The collision factor observation and the factor of collision in the video frame of measured value and collision factor predicted value and the second moving object collection
Predicted value, wherein the first moving object collection is the set of moving object in the first testing result, second moving object
Collection is the set of moving object in the second testing result；To the first moving object collection and the second moving object collection and accordingly
Collision factor observation and collision factor predicted value matched, determine the moving object for including in the video and its correspondence
Collision factor observation and collision factor predicted value；And according to Kalman filtering algorithm, based on including in the video
The corresponding collision factor observation of moving object and collision factor predicted value, calculate the collision factor information of the moving object.
12. equipment according to claim 9, wherein first detection device is additionally operable to based on deep learning technology
When detecting the moving object for including in the video, the entity type of the moving object is determined.
13. equipment according to claim 12, wherein the judgment means are additionally operable in determining collide, according to
The entity type and collision factor information of the moving object determine the severity of the secondary collision.
14. equipment according to claim 13, wherein the judgment means, for determine collide when, described
Extraction is related to the associated video frame of the secondary accident in video, according to the entity type of the moving object and the associated video
The collision factor information of the movement entity in frame determines the severity of the secondary collision.
15. equipment according to claim 14, first detection device are additionally operable to detect institute based on deep learning technology
State the additional Event information in associated video frame；
The judgment means are used for the entity type according to the moving object, the movement reality in the associated video frame
The collision factor information and additional Event information of body, determine the severity of the secondary collision.
16. equipment according to claim 9, wherein the input unit, for obtaining the photographic device by being fixedly installed
The video to be detected of shooting.
17. a kind of crash detection device, wherein the equipment includes：
Processor；And
One or more machine readable medias of machine readable instructions are stored with, when the processor executes the machine readable finger
When enabling so that the equipment executes such as method described in any item of the claim 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810106578.3A CN108458691B (en) | 2018-02-02 | 2018-02-02 | A kind of collision checking method and equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810106578.3A CN108458691B (en) | 2018-02-02 | 2018-02-02 | A kind of collision checking method and equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108458691A true CN108458691A (en) | 2018-08-28 |
| CN108458691B CN108458691B (en) | 2019-04-19 |
Family
ID=63239296
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810106578.3A Active CN108458691B (en) | 2018-02-02 | 2018-02-02 | A kind of collision checking method and equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108458691B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110276959A (en) * | 2019-06-26 | 2019-09-24 | 奇瑞汽车股份有限公司 | Processing method, device and the storage medium of traffic accident |
| CN112446358A (en) * | 2020-12-15 | 2021-03-05 | 北京京航计算通讯研究所 | Target detection method based on video image recognition technology |
| CN112507913A (en) * | 2020-12-15 | 2021-03-16 | 北京京航计算通讯研究所 | Target detection system based on video image recognition technology |
| CN112651377A (en) * | 2021-01-05 | 2021-04-13 | 河北建筑工程学院 | Ice and snow movement accident detection method and device and terminal equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103226891A (en) * | 2013-03-26 | 2013-07-31 | 中山大学 | Video-based vehicle collision accident detection method and system |
| CN103258432A (en) * | 2013-04-19 | 2013-08-21 | 西安交通大学 | Traffic accident automatic identification processing method and system based on videos |
| CN106127114A (en) * | 2016-06-16 | 2016-11-16 | 北京数智源科技股份有限公司 | Intelligent video analysis method |
-
2018
- 2018-02-02 CN CN201810106578.3A patent/CN108458691B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103226891A (en) * | 2013-03-26 | 2013-07-31 | 中山大学 | Video-based vehicle collision accident detection method and system |
| CN103258432A (en) * | 2013-04-19 | 2013-08-21 | 西安交通大学 | Traffic accident automatic identification processing method and system based on videos |
| CN106127114A (en) * | 2016-06-16 | 2016-11-16 | 北京数智源科技股份有限公司 | Intelligent video analysis method |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110276959A (en) * | 2019-06-26 | 2019-09-24 | 奇瑞汽车股份有限公司 | Processing method, device and the storage medium of traffic accident |
| CN112446358A (en) * | 2020-12-15 | 2021-03-05 | 北京京航计算通讯研究所 | Target detection method based on video image recognition technology |
| CN112507913A (en) * | 2020-12-15 | 2021-03-16 | 北京京航计算通讯研究所 | Target detection system based on video image recognition technology |
| CN112651377A (en) * | 2021-01-05 | 2021-04-13 | 河北建筑工程学院 | Ice and snow movement accident detection method and device and terminal equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108458691B (en) | 2019-04-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108458691B (en) | A kind of collision checking method and equipment | |
| Hu et al. | Joint monocular 3D vehicle detection and tracking | |
| KR102205096B1 (en) | Transaction risk detection method and apparatus | |
| US20200290608A1 (en) | Forward collision control method and apparatus, electronic device, program, and medium | |
| Essa et al. | Simulated traffic conflicts: do they accurately represent field-measured conflicts? | |
| Bai et al. | Traffic anomaly detection via perspective map based on spatial-temporal information matrix. | |
| CN103927508B (en) | Target vehicle tracking method and device | |
| CN105513349B (en) | Mountainous area highway vehicular events detection method based on double-visual angle study | |
| CN111771207A (en) | Enhanced vehicle tracking | |
| CN104463903A (en) | Pedestrian image real-time detection method based on target behavior analysis | |
| Alozi et al. | Evaluating the safety of autonomous vehicle–pedestrian interactions: An extreme value theory approach | |
| CN110032947A (en) | A kind of method and device that monitor event occurs | |
| Pyo et al. | Front collision warning based on vehicle detection using CNN | |
| JP2013174568A (en) | Moving body tracking device, moving body tracking method, and program | |
| US11748593B2 (en) | Sensor fusion target prediction device and method for vehicles and vehicle including the device | |
| CN109766867A (en) | Travel condition of vehicle determines method, apparatus, computer equipment and storage medium | |
| CN111497741B (en) | Collision early warning method and device | |
| CN110097121A (en) | A kind of classification method of driving trace, device, electronic equipment and storage medium | |
| KR20180127245A (en) | Method for Predicting Vehicle Collision Using Data Collected from Video Games | |
| CN114972911A (en) | Method and equipment for collecting and processing output data of automatic driving perception algorithm model | |
| Guo et al. | The efficacy of neural planning metrics: A meta-analysis of pkl on nuscenes | |
| Guerrieri et al. | Real-time social distance measurement and face mask detection in public transportation systems during the COVID-19 pandemic and post-pandemic Era: Theoretical approach and case study in Italy | |
| JP2021043146A (en) | Obstacle detection system and obstacle detection method | |
| US10417358B2 (en) | Method and apparatus of obtaining feature information of simulated agents | |
| Lim et al. | Event-driven track management method for robust multi-vehicle tracking |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |