CN102663368A - Moving target regional integration and optimization method on basis of Gestalt visual principle - Google Patents
Moving target regional integration and optimization method on basis of Gestalt visual principle Download PDFInfo
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- CN102663368A CN102663368A CN2012101110160A CN201210111016A CN102663368A CN 102663368 A CN102663368 A CN 102663368A CN 2012101110160 A CN2012101110160 A CN 2012101110160A CN 201210111016 A CN201210111016 A CN 201210111016A CN 102663368 A CN102663368 A CN 102663368A
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Abstract
The invention provides a moving target regional integration and optimization method on the basis of a Gestalt visual principle, which comprises a regularly abstracting stage and an integrating and optimizing stage. In the moving target regional integration and optimization method on the basis of the Gestalt visual principle, which is disclosed by the invention, according to two principles of the Gestalt visual principle, rules for integrating two target vehicle regions are abstracted; and when two targets meet all integration conditions, the two targets are integrated into one target to return the integrated features of the two targets.
Description
Technical field
The present invention relates to a kind of moving target integrated optimization method, relate in particular to a kind of motion target area integrated optimization method based on the Gestalt visual theory.
Background technology
Intelligent traffic management systems (ITS:Intelligent Traffic System) is the focus that present countries in the world traffic and transport field is is competitively researched and developed.Its main target is to obtain road information and vehicle behavioural information, comprises vehicle flowrate, the speed of a motor vehicle, roadway occupancy, traffic hazard detection etc.What in image, carry out the regional detection of moving vehicle is one of important technology of intelligent transportation system, is the core methed of realizing the ITS target, also is that ITS realizes robotization, intellectuality and real-time key in application.
In conventional target detection method based on background subtraction, because interference such as illumination and body colors, the phenomenon of " over-segmentation " usually can appear, show as and on the result who detects, occur a vehicle is divided into several disconnected zones.Yet; Use connected component analysis to distinguish the method for different vehicle target; Finally can these disconnected zones (be belonged to a moving vehicle originally; Owing to " over-segmentation " is divided into several sections) be designated the different vehicles target, had a strong impact on the correctness that detects, it is very unfavorable that follow-up tracking to vehicle target is reached more senior video analysis behavior.Therefore it is very important that the problem that adopts certain method to solve " over-segmentation " seems.
Summary of the invention
The object of the invention is to provide a kind of motion target area integrated optimization method based on the Gestalt visual theory, to improve the correctness of moving object detection.
For reaching above-mentioned purpose, the motion target area integrated optimization method based on the Gestalt visual theory of the present invention comprises the steps:
1) the rule level of abstration straction:
A, the proximity principle that the Gestalt visual theory is followed and closed principle are abstract integrates required satisfied integrated optimization rule for target; Promptly selected characteristic parameter; And be the characteristic parameter setting threshold, when characteristic parameter and threshold value satisfy when imposing a condition, integrate two targets;
B, end.
2) the integrated optimization stage
All target vehicle zones that a, the extraction of collection target obtain are to waiting to integrate among the formation Q;
Two target t among b, the calculating Q
1And t
2The integration characteristics parameter, if all integration parameters of this two targets can both satisfy the integrated optimization rule, then two targets are merged into a target t
New, in Q, delete t
1And t
2And adding t
NewOtherwise, continue to seek can and t
1The target of integrating;
C, circulation execution in step 2)-b, all can not satisfy the integration condition up to the integration characteristics parameter of any two targets;
D, end.
Further, among the present invention, the characteristic ginseng value described in step 1)-a comprises width and rise, wide ratio of similitude and high ratio of similitude, area coverage coefficient, with two target t
1And t
2The rise threshold value setting be the picture height 1/10th with the width threshold value setting be 1/10th of picture width, high ratio of similitude Ratio
HWith wide ratio of similitude Ratio
WAll be set to 0.7, the area coverage coefficient is set to 0.5, when width and rise respectively less than rise threshold values and width threshold values, high ratio of similitude Ratio
HWith wide ratio of similitude Ratio
WAll greater than 0.7 and the area coverage coefficient greater than 0.5 o'clock, integrate two target t
1And t
2
Among the present invention, step 2)-a among the Q each target vehicle zone comprise the boundary rectangle of vehicle region, the information such as particular location in image.
Further, among the present invention, step 2)-detailed process of b is following:
To two target t among the Q
1And t
2, calculate the integration characteristics parameter value of these two targets respectively, comprise width and rise (is unit with the pixel), wide ratio of similitude and high ratio of similitude, area coverage coefficient.
Judge t
1And t
2All integration characteristics parameters whether all meet the integration condition.If, then with t
1And t
2Merge into t
New, with t
1And t
2In formation Q, delete, add t simultaneously
NewBe the fresh target zone; Otherwise, cycle criterion t
1With whether can integrating of remaining target;
Further, among the present invention, step 2)-c in when finding that all targets all can not satisfy the integration condition in the current queue, then finish scan queue; Return integrated results; Finish to integrate.
Beneficial effect of the present invention is following: the motion target area integrated optimization method based on the Gestalt visual theory of the present invention; Proximity principle and closed principle according to the Gestalt visual theory; Take out rule with two target vehicle Regional Integration; When two targets satisfy all integration conditions, two targets are integrated into a target, return this this overall permanence that has of two targets itself.
Description of drawings
Fig. 1 is the Intelligent traffic management systems workflow diagram.
Fig. 2 is the process flow diagram of the motion target area integrated optimization method based on the Gestalt visual theory of the present invention.
Fig. 3 is the detail flowchart based on the motion target area integrated optimization method of Gestalt visual theory.
Embodiment
In order more to understand technology contents of the present invention, special act specific embodiment also cooperates appended graphic explanation following.
As shown in Figure 1, Intelligent traffic management systems is obtained vedio data through video image acquisition equipment, and through the image pre-service, moving vehicle detects, target is integrated, and carries out moving vehicle then and follows the tracks of and follow-up advanced processes.The moving target integrated optimization is great to back total system influence.
Vehicle detection is the core procedure of Intelligent traffic management systems; The appearance of " over-segmentation " phenomenon is difficult to avoid when carrying out vehicle detection through the method for background subtraction; Thinking of the present invention is exactly through back by its whole phenomenon of the vehicle of over-segmentation recovery to detecting, thereby improves the performance of whole Intelligent traffic management systems.The vehicle target integration process is the process flow diagram of the motion target area integrated optimization method based on the Gestalt visual theory of the present invention, and is as shown in Figure 2.
In the rule level of abstration straction (step 1); Two cardinal principles with the Gestalt visual theory; Being that proximity principle and closed principle are abstract integrates required satisfied integrated optimization rule for target, promptly selects characteristic parameter, and is these characteristic parameter setting thresholds; In order to integrating the foundation of two targets, the integration characteristics parameter comprises width and rise, wide ratio of similitude and high ratio of similitude, area coverage coefficient; In the present embodiment, with two target t
1And t
2The rise threshold value setting be that 70 pixel sizes (picture height 1/10th) and width threshold value setting are 50 pixel sizes (picture width 1/10th), high ratio of similitude Ratio
HWith wide ratio of similitude Ratio
WAll be set to 0.7, the area coverage coefficient is set to 0.5, when width and rise respectively less than rise threshold values and width threshold values, high ratio of similitude Ratio
HWith wide ratio of similitude Ratio
WAll greater than 0.7 and the area coverage coefficient greater than 0.5 o'clock, integrate two target t
1And t
2
At integrated optimization stage (step 2-4), step 2 is collected the information of all moving vehicles in the current image frame, and these moving vehicle information are kept in the formation;
Fig. 3 is the detail flowchart based on the motion target area integrated optimization method of Gestalt visual theory.
Whether the integration parameters value that step 35 determining step 34 calculates satisfies the integration condition, if then turn to step 36, otherwise turn to 37;
Whether identical step 39 judge continuous two-wheeled scanning result (i.e. i wheel scan result and i-1 wheel contrast, i 1).If the two-wheeled result is different, then continues next round and integrate; Otherwise, turn to 3a;
Step 3a finishes.
Motion target area integrated optimization method based on the Gestalt visual theory of the present invention; Proximity principle and closed principle according to the Gestalt visual theory; Take out rule with two target vehicle Regional Integration; When two targets satisfy all integration conditions, two targets are integrated into a target, return this this overall permanence that has of two targets itself.
Though the present invention discloses as above with preferred embodiment, so it is not in order to limit the present invention.Have common knowledge the knowledgeable in the technical field under the present invention, do not breaking away from the spirit and scope of the present invention, when doing various changes and retouching.Therefore, protection scope of the present invention is as the criterion when looking claims person of defining.
Claims (5)
1. the motion target area integrated optimization method based on the Gestalt visual theory is characterized in that, comprises the steps:
1) the rule level of abstration straction:
A, the proximity principle that the Gestalt visual theory is followed and closed principle are abstract integrates required satisfied integrated optimization rule for target; Promptly selected characteristic parameter; And be the characteristic parameter setting threshold, when characteristic parameter and threshold value satisfy when imposing a condition, integrate two targets;
B, end.
2) the integrated optimization stage
All target vehicle zones that a, the extraction of collection target obtain are to waiting to integrate among the formation Q;
Two target t among b, the calculating Q
1And t
2The integration characteristics parameter, if all integration parameters of this two targets can both satisfy the integrated optimization rule, then two targets are merged into a target t
New, in Q, delete t
1And t
2And adding t
NewOtherwise, continue to seek can and t
1The target of integrating;
C, circulation execution in step 2)-b, all can not satisfy the integration condition up to the integration characteristics parameter of any two targets;
D, end.
2. the motion target area integrated optimization method based on the Gestalt visual theory according to claim 1; It is characterized in that; Wherein the characteristic ginseng value described in step 1)-a comprises width and rise, wide ratio of similitude and high ratio of similitude, area coverage coefficient, with two target t
1And t
2The rise threshold value setting be the picture height 1/10th with the width threshold value setting be 1/10th of picture width, high ratio of similitude Ratio
HWith wide ratio of similitude Ratio
WAll be set to 0.7, the area coverage coefficient is set to 0.5, when width and rise respectively less than rise threshold values and width threshold values, high ratio of similitude Ratio
HWith wide ratio of similitude Ratio
WAll greater than 0.7 and the area coverage coefficient greater than 0.5 o'clock, integrate two target t
1And t
2
3. the motion target area integrated optimization method based on the Gestalt visual theory according to claim 1; It is characterized in that, wherein step 2)-a among the Q each target vehicle zone comprise the boundary rectangle of vehicle region, the information such as particular location in image.
4. the motion target area integrated optimization method based on the Gestalt visual theory according to claim 1 is characterized in that, wherein step 2)-detailed process of b is following:
To two target t among the Q
1And t
2, calculate the integration characteristics parameter of these two targets respectively;
Judge t
1And t
2All integration characteristics parameters whether all meet the integrated optimization rule, if, then with t
1And t
2Merge into t
New, with t
1And t
2In formation Q, delete, add t simultaneously
NewBe the fresh target zone; Otherwise, cycle criterion t
1With whether can integrating of remaining target.
5. based on the described motion target area integrated optimization method of claim 1, it is characterized in that step 2 based on the Gestalt visual theory)-c in when finding that all targets all can not satisfy the integrated optimization rule in the current queue, then finish scan queue; Return integrated results; Finish to integrate.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109615874A (en) * | 2018-12-28 | 2019-04-12 | 浙江大学 | A kind of road condition analyzing method based on Gestalt psychology criterion |
| CN111461139A (en) * | 2020-03-27 | 2020-07-28 | 武汉工程大学 | Multi-target visual saliency layered detection method in complex scene |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090116728A1 (en) * | 2007-11-07 | 2009-05-07 | Agrawal Amit K | Method and System for Locating and Picking Objects Using Active Illumination |
| CN102004922A (en) * | 2010-12-01 | 2011-04-06 | 南京大学 | High-resolution remote sensing image plane extraction method based on skeleton characteristic |
| US20110175905A1 (en) * | 2010-01-15 | 2011-07-21 | American Propertunity, LLC. | Infoshape: displaying multidimensional information |
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2012
- 2012-04-16 CN CN201210111016.0A patent/CN102663368B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090116728A1 (en) * | 2007-11-07 | 2009-05-07 | Agrawal Amit K | Method and System for Locating and Picking Objects Using Active Illumination |
| US20110175905A1 (en) * | 2010-01-15 | 2011-07-21 | American Propertunity, LLC. | Infoshape: displaying multidimensional information |
| CN102004922A (en) * | 2010-12-01 | 2011-04-06 | 南京大学 | High-resolution remote sensing image plane extraction method based on skeleton characteristic |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109615874A (en) * | 2018-12-28 | 2019-04-12 | 浙江大学 | A kind of road condition analyzing method based on Gestalt psychology criterion |
| CN109615874B (en) * | 2018-12-28 | 2021-02-02 | 浙江大学 | Road condition analysis method based on form tower psychological criterion |
| CN111461139A (en) * | 2020-03-27 | 2020-07-28 | 武汉工程大学 | Multi-target visual saliency layered detection method in complex scene |
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Address after: Hongqiao Industrial Park in Taixing city of Jiangsu province Taizhou city 225400 six Wei Hong Kong Avenue Patentee after: Nanjing University Address before: 210093 Nanjing, Gulou District, Jiangsu, No. 22 Hankou Road Patentee before: Nanjing University |
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