CN106339723A - Video based river illegal dredging detection method - Google Patents
Video based river illegal dredging detection method Download PDFInfo
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- CN106339723A CN106339723A CN201610753329.4A CN201610753329A CN106339723A CN 106339723 A CN106339723 A CN 106339723A CN 201610753329 A CN201610753329 A CN 201610753329A CN 106339723 A CN106339723 A CN 106339723A
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- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
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Abstract
The invention discloses a video based river illegal dredging detection method. The method comprises the following steps that time-domain modeling is carried out on surrounding neighborhood of each pixel in an image, and a foreground image is determined; the foreground image is utilized to carry out mass block detection, and object fusion is carried out on adjacent foreground points to obtain a candidate object area; the amount and type of objects in the object area are detected via a model; whether each object is an alarm object is determined according to a preset rule, a moving track and staying time. According to the detection method, time-domain modeling is carried out on each pixel in the image to determine foreground points, object fusion is carried out on the adjacent foreground points to obtain the candidate object area, a multi-type classifier is used to classify the objects, the classified objects as well as moving track and staying time thereof are determined, the accuracy of object intrusion determination is improved, and false alarm and alarm omission in alarm detection are avoided.
Description
Technical field
The invention belongs to the technical field of video monitoring is and in particular to detection method based on the illegal mining of video river course.
Background technology
In safety monitoring, the application of video monitoring is quite varied, leads to multipair video and is analyzed it can be determined that going out motion
The way of act of target, and then warning reminding is carried out to the Deviant Behavior of target, to prevent accident.
It is used widely based on the warning function of video, but due to by complicated outdoor environment, light, day and night changing
Interference, the false alarm that simple function produces and to fail to report alert problem ratio more serious, which prevent the illegal mining of video river course and steal and unload inspection
The large-scale use surveyed.
Content of the invention
The present invention is to solve the problems, such as that prior art proposes, and its objective is to provide one kind to be based on the illegal mining of video river course
Detection method.
The technical scheme is that a kind of detection method based on the illegal mining of video river course, comprise the following steps:
() carries out the time domain modeling of surrounding neighbors to each pixel in image, judges foreground image；
() utilizes the foreground image in step (), carries out mass detection, adjacent foreground point is carried out subject fusion, obtains
Candidate target region；
() detects target number and type in step () target area with model method；
According to default rule, movement locus and the time of staying, () judges whether each target is alarm target.
Described step () when time domain modeling is carried out to each pixel, by this pixel value and its time domain Gauss model point
Cloth compares, and when this pixel value changes exceedes the standard deviation of 3 times of Gauss model, judges this pixel for foreground pixel point, otherwise for
Background pixel；Update the Model in Time Domain of each pixel by fixing frame per second simultaneously.
According to the spatial relationship between foreground pixel point, each foreground pixel point is clustered nearby；The result of cluster
Then as the result of agglomerate, i.e. candidate target.
Using polymorphic type grader, candidate target is carried out with traversal detection in described step () model method and obtain each
The target number of candidate target and target type.
The result that described polymorphic type grader produces is: sand dredger type, floating thing type, other types；Traversal detection
Method as follows:
A trains: carries out characteristic vector pickup to sand dredger sample, floating thing sample and negative sample, and is input to polymorphic type classification
Carry out machine learning in device, preserve sample pattern；
B identifies: input picture, and extracts the characteristic vector of candidate target, and grader loads sample pattern the feature to input
Vector is identified, to judge the type of target.
Judge whether the target type being drawn by step () suits the requirements the target type of warning, target type meets
When, continue to judge this target trajectory and default rule relation, when the target stay time meeting alert if, produce and report to the police.
Described rule, with the polygonal profile of Mulit-point Connection one-tenth as basis for estimation, judges including target type, target is moved
Track and the judgement of target stay time.
The river course illegal mining based on video for the present invention is stolen and is unloaded in detection method, each pixel in image is carried out time domain modeling with
Judge foreground point, neighbouring foreground point is carried out subject fusion and obtains candidate target region, by polymorphic type grader to target
Carry out target classification, then sorted target and its movement locus and the time of staying are judged, thus improve target
Invade the accuracy judging, it is to avoid in alarm detection, report and fail to report the generation of situation by mistake.
Brief description
Fig. 1 is method of the present invention flow chart；
Fig. 2 is the pixel value schematic diagram in the present invention, a certain pixel being carried out during time domain modeling；
Fig. 3 is the traversal detection process schematic diagram of polymorphic type grader in the present invention；
Fig. 4 is the judgement schematic diagram that the present invention implements to report to the police.
Specific embodiment
Hereinafter, referring to the drawings and embodiment the present invention is described in detail:
As shown in figure 1, a kind of detection method based on the illegal mining of video river course, comprise the following steps:
() carries out the time domain modeling of surrounding neighbors to each pixel in image, judges foreground image；
() utilizes the foreground image in step (), carries out mass detection, adjacent foreground point is carried out subject fusion, obtains
Candidate target region；
() detects target number and type in step () target area with model method；
According to default rule, movement locus and the time of staying, () judges whether each target is alarm target.
Described step () when time domain modeling is carried out to each pixel, by this pixel value and its time domain Gauss model point
Cloth compares, and when this pixel value changes exceedes the standard deviation of 3 times of Gauss model, judges this pixel for foreground pixel point, otherwise for
Background pixel；Update the Model in Time Domain of each pixel by fixing frame per second simultaneously.
As shown in Fig. 2 this pixel value changes is 80 in this example, and the standard deviation of its Gauss modeling is 20, visually this picture
Plain value is undergone mutation, and regards as foreground pixel point；Update the Model in Time Domain of each pixel by fixing frame per second simultaneously, up-to-date to obtain
Pixel value.
According to the spatial relationship between foreground pixel point, each foreground pixel point is clustered nearby；The result of cluster
Then as the result of agglomerate, i.e. candidate target.
Using polymorphic type grader, candidate target is carried out with traversal detection in described step () model method and obtain each
The target number of candidate target and target type.
As shown in figure 3, the result that described polymorphic type grader produces is: sand dredger type, floating thing type, other classes
Type；The method of traversal detection is as follows:
A trains: carries out characteristic vector pickup to sand dredger sample, floating thing sample and negative sample, and is input to polymorphic type classification
Carry out machine learning in device, preserve sample pattern；
B identifies: input picture, and extracts the characteristic vector of candidate target, and grader loads sample pattern the feature to input
Vector is identified, to judge the type of target.
Judge whether the target type being drawn by step () suits the requirements the target type of warning, target type meets
When, continue to judge this target trajectory and default rule relation, when the target stay time meeting alert if, produce and report to the police.
The polygonal profile that default rule is become with Mulit-point Connection, as basis for estimation, judges including target type and target fortune
Flowing mode judges, target motion mode judges to include judging whether the movement locus of target enter above-mentioned polygonal profile, sentence
Whether the movement locus of disconnected target leave above-mentioned polygonal profile or movement locus the stopping in polygonal profile judging target
Stay the time.
As shown in figure 4, judging target trajectory in the range of rule settings, during the stop of the type of target and target
Between all reach alert if, target can produce illegal mining report to the police.
The river course illegal mining based on video for the present invention is stolen and is unloaded in detection method, each pixel in image is carried out time domain modeling with
Judge foreground point, neighbouring foreground point is carried out subject fusion and obtains candidate target region, by polymorphic type grader to target
Carry out target classification, then sorted target and its movement locus and the time of staying are judged, thus improve target
Invade the accuracy judging, it is to avoid in alarm detection, report and fail to report the generation of situation by mistake.
Claims (7)
1. a kind of detection method based on the illegal mining of video river course it is characterised in that: comprise the following steps:
() carries out the time domain modeling of surrounding neighbors to each pixel in image, judges foreground image；
() utilizes the foreground image in step (), carries out mass detection, adjacent foreground point is carried out subject fusion, obtains
Candidate target region；
() detects target number and type in step () target area with model method；
According to default rule, movement locus and the time of staying, () judges whether each target is alarm target.
2. the detection method based on the illegal mining of video river course according to claim 1 it is characterised in that: described step () exists
When time domain modeling is carried out to each pixel, this pixel value is compared with the distribution of its time domain Gauss model, when this pixel value changes
Exceed 3 times of Gauss model of standard deviation, judge this pixel for foreground pixel point, otherwise for background pixel；Press simultaneously and fix
Frame per second updates the Model in Time Domain of each pixel.
3. the detection method based on the illegal mining of video river course according to claim 2 it is characterised in that: according to foreground pixel point
Between spatial relationship, each foreground pixel point is clustered nearby；The result then result as agglomerate of cluster, i.e. candidate
Target.
4. the detection method based on the illegal mining of video river course according to claim 1 it is characterised in that: described step () mould
Candidate target is carried out travel through target number and the mesh that detection obtains each candidate target using polymorphic type grader in type method
Mark type.
5. the detection method based on the illegal mining of video river course according to claim 4 it is characterised in that: the classification of described polymorphic type
The result that device produces is: sand dredger type, floating thing type, other types；The method of traversal detection is as follows:
A trains: carries out characteristic vector pickup to sand dredger sample, floating thing sample and negative sample, and is input to polymorphic type classification
Carry out machine learning in device, preserve sample pattern；
B identifies: input picture, and extracts the characteristic vector of candidate target, and grader loads sample pattern the feature to input
Vector is identified, to judge the type of target.
6. the detection method based on the illegal mining of video river course according to claim 1 ~ 5 it is characterised in that: judge by step
Whether the target type that () draws suits the requirements the target type of warning, when target type meets, continues to judge this target track
Mark and default rule relation, when the target stay time meeting alert if, produce and report to the police.
7. the detection method based on the illegal mining of video river course according to claim 6 it is characterised in that: described rule with multiple spot
The polygonal profile connecting into is basis for estimation, including sentencing of target type judgement, target trajectory and target stay time
Disconnected.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107679524A (en) * | 2017-10-31 | 2018-02-09 | 天津天地伟业信息系统集成有限公司 | A kind of detection method of the safety cap wear condition based on video |
| CN107911650A (en) * | 2017-11-01 | 2018-04-13 | 炜呈智能电力科技（杭州）有限公司 | Intelligent watercourse monitoring system |
| CN107992902A (en) * | 2017-12-22 | 2018-05-04 | 北京工业大学 | A kind of routine bus system based on supervised learning steals individual automatic testing method |
| CN108010063A (en) * | 2017-12-27 | 2018-05-08 | 天津天地伟业投资管理有限公司 | A kind of moving target based on video enters or leaves the detection method in region |
| CN109359573A (en) * | 2018-09-30 | 2019-02-19 | 天津天地伟业投资管理有限公司 | A kind of warning method and device based on the separation of accurate people's vehicle |
| CN110555418A (en) * | 2019-09-08 | 2019-12-10 | 无锡高德环境科技有限公司 | AI target object identification method and system for water environment |
| CN110942577A (en) * | 2019-11-04 | 2020-03-31 | 佛山科学技术学院 | Machine vision-based river sand stealing monitoring system and method |
| CN117152892A (en) * | 2023-09-25 | 2023-12-01 | 联通(广东)产业互联网有限公司 | Sand theft prevention method and system based on video monitoring identification |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101119482A (en) * | 2007-09-28 | 2008-02-06 | 北京智安邦科技有限公司 | Overall view monitoring method and apparatus |
| US20100045799A1 (en) * | 2005-02-04 | 2010-02-25 | Bangjun Lei | Classifying an Object in a Video Frame |
| CN105512666A (en) * | 2015-12-16 | 2016-04-20 | 天津天地伟业数码科技有限公司 | River garbage identification method based on videos |
| CN105744232A (en) * | 2016-03-25 | 2016-07-06 | 南京第五十五所技术开发有限公司 | Method for preventing power transmission line from being externally broken through video based on behaviour analysis technology |
-
2016
- 2016-08-30 CN CN201610753329.4A patent/CN106339723A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100045799A1 (en) * | 2005-02-04 | 2010-02-25 | Bangjun Lei | Classifying an Object in a Video Frame |
| CN101119482A (en) * | 2007-09-28 | 2008-02-06 | 北京智安邦科技有限公司 | Overall view monitoring method and apparatus |
| CN105512666A (en) * | 2015-12-16 | 2016-04-20 | 天津天地伟业数码科技有限公司 | River garbage identification method based on videos |
| CN105744232A (en) * | 2016-03-25 | 2016-07-06 | 南京第五十五所技术开发有限公司 | Method for preventing power transmission line from being externally broken through video based on behaviour analysis technology |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107679524A (en) * | 2017-10-31 | 2018-02-09 | 天津天地伟业信息系统集成有限公司 | A kind of detection method of the safety cap wear condition based on video |
| CN107911650A (en) * | 2017-11-01 | 2018-04-13 | 炜呈智能电力科技（杭州）有限公司 | Intelligent watercourse monitoring system |
| CN107992902A (en) * | 2017-12-22 | 2018-05-04 | 北京工业大学 | A kind of routine bus system based on supervised learning steals individual automatic testing method |
| CN108010063A (en) * | 2017-12-27 | 2018-05-08 | 天津天地伟业投资管理有限公司 | A kind of moving target based on video enters or leaves the detection method in region |
| CN109359573A (en) * | 2018-09-30 | 2019-02-19 | 天津天地伟业投资管理有限公司 | A kind of warning method and device based on the separation of accurate people's vehicle |
| CN110555418A (en) * | 2019-09-08 | 2019-12-10 | 无锡高德环境科技有限公司 | AI target object identification method and system for water environment |
| CN110942577A (en) * | 2019-11-04 | 2020-03-31 | 佛山科学技术学院 | Machine vision-based river sand stealing monitoring system and method |
| CN117152892A (en) * | 2023-09-25 | 2023-12-01 | 联通(广东)产业互联网有限公司 | Sand theft prevention method and system based on video monitoring identification |
| CN117152892B (en) * | 2023-09-25 | 2024-03-19 | 联通(广东)产业互联网有限公司 | Sand theft prevention method and system based on video monitoring identification |
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