CN109445453A - A kind of unmanned plane Real Time Compression tracking based on OpenCV - Google Patents
A kind of unmanned plane Real Time Compression tracking based on OpenCV Download PDFInfo
- Publication number
- CN109445453A CN109445453A CN201811063858.7A CN201811063858A CN109445453A CN 109445453 A CN109445453 A CN 109445453A CN 201811063858 A CN201811063858 A CN 201811063858A CN 109445453 A CN109445453 A CN 109445453A
- Authority
- CN
- China
- Prior art keywords
- target
- unmanned plane
- tracking
- opencv
- real time
- 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.)
- Pending
Links
- 230000006835 compression Effects 0.000 title claims abstract description 21
- 238000007906 compression Methods 0.000 title claims abstract description 21
- 238000001514 detection method Methods 0.000 claims abstract description 17
- 239000000284 extract Substances 0.000 claims abstract description 7
- 238000012549 training Methods 0.000 claims abstract description 7
- 238000012545 processing Methods 0.000 claims abstract description 4
- 230000009467 reduction Effects 0.000 claims description 16
- 238000000034 method Methods 0.000 claims description 12
- 238000006073 displacement reaction Methods 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 3
- 230000003287 optical effect Effects 0.000 claims description 2
- 238000005352 clarification Methods 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 6
- 230000000007 visual effect Effects 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 230000010365 information processing Effects 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 239000000700 radioactive tracer Substances 0.000 description 1
- 238000011946 reduction process Methods 0.000 description 1
- 235000015170 shellfish Nutrition 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
- H04B7/185—Space-based or airborne stations; Stations for satellite systems
- H04B7/18502—Airborne stations
- H04B7/18506—Communications with or from aircraft, i.e. aeronautical mobile service
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/14—Picture signal circuitry for video frequency region
- H04N5/144—Movement detection
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/183—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a single remote source
- H04N7/185—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a single remote source from a mobile camera, e.g. for remote control
Landscapes
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Multimedia (AREA)
- Physics & Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- General Physics & Mathematics (AREA)
- Astronomy & Astrophysics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Image Analysis (AREA)
Abstract
A kind of unmanned plane Real Time Compression tracking based on OpenCV, it include: after selection unmanned plane tracks target, picture frame is converted by video, extract the sample data of image sequence background and target, carry out initial frame processing, characteristic point detection and matching are carried out to sequence of video images based on openCV, extract clarification of objective vector to be detected；During tracking, classifier is trained using treated feature vector as training set, each frame image inputted later is all trained using the trained classifier of previous frame, show that target window realizes tracking；The difference that the real standard distance of unmanned plane and target position is calculated according to the position coordinates of tracking target inputs monocycle position ring PID controller, and unmanned plane is controlled by position ring and carries out position adjustment, realizes the tracking to mobile target.
Description
Technical field
The present invention relates to Intelligent Information Processing and unmanned plane tracer technique field, specially a kind of nobody based on OpenCV
Machine Real Time Compression tracking.
Background technique
The movable object tracking technology of view-based access control model, refer to the image sequence that obtains robot by visual sensor into
Row analysis, usually progress target detection, complete target identification, finally track to the target identified, obtain target
The information such as real-time spatial position, target size size, velocity and acceleration, and the motion profile of mobile target can be obtained.By
It is mature in the target recognition and tracking algorithm based on image, track it is at low cost, the advantages that precision height and strong antijamming capability, therefore
There is extensive and actual application in many fields.
With being gradually expanded for unmanned plane application range, the requirement for UAV system autonomous control is also constantly being mentioned
Height, unmanned plane automatic control system carry out Intelligent Information Processing by perception external environment, automatically generate corresponding control strategy,
It realizes various demand tasks, and there is effective and quick adaptive ability, the especially commanding visual angle of unmanned plane,
Can be with large-range monitoring surface state, while the place that personnel are not easy to be related to can be quickly reached, efficient implementing monitoring reduces
Corresponding personnel risk, this makes unmanned plane start to be widely used in security industry, it is well known that visualized management substantially according to
The problem of completing, and as user is to the good application of equipment by fixed monitoring device, monitoring dead angle can not avoid, therefore
The place of some bad environments, the installation wirings of the equipment such as video camera and maintenance are all big problems, and fixed point video monitoring is can
Under trend depending on changing the management domain diversification of demand, needs the new equipment as unmanned plane and provide under special circumstances
Technical guarantee.
Summary of the invention
The purpose of the present invention is to provide a kind of unmanned plane Real Time Compression tracking based on OpenCV, solves above-mentioned back
The problem of being proposed in scape.
The present invention mainly carries out demand using moving Object Detection algorithm, target tracking algorithm and tracking control unit level
Analysis, design object detection and tracking algorithm using the moving Object Detection device based on characteristic point and are based on compressive sensing theory
Target tracking with improve algorithm tracking accuracy and it is anti-short-term block ability, monocycle position ring PID controller is designed, by nothing
The man-machine distance of the real standard direction between target as input to the controller, realizes the mobile target of unmanned plane tracking.
A kind of unmanned plane Real Time Compression tracking based on OpenCV, which comprises
Step S1: unmanned plane takes off, and starts to acquire image, passback video data to the end PC；
Step S2: after selection unmanned plane tracking target, picture frame is converted by video；
Step S3: characteristic point detection and matching are carried out to sequence of video images based on openCV, extract target to be detected
Feature vector；
Step S4: extracting the sample data of image sequence background and target, carries out initial frame processing；
Step S5: feature vector is compressed using sparseness measuring matrix, is converted；
Step S6: during tracking, classifier is trained using treated feature vector as training set, later
Each frame image of input is all trained using the trained classifier of previous frame, show that target window realizes tracking；
Step S7: after obtaining tracking target, the position coordinates of target are calculated；
Step S8: according to the position coordinates of tracking target, the two is calculated in vertical direction in conjunction with unmanned plane altitude information
On horizontal displacement；
Step S9: using the difference of unmanned plane and the real standard distance of mobile target as the PID of unmanned plane position control
Parameter is inputted, unmanned plane is controlled by position ring and carries out position adjustment, realizes the tracking to mobile target.
Further, in step S3, the Fast Feature Detector function setup detection threshold value of openCV is called to mention
Angle point color, the Texture eigenvalue for taking target to be detected, establish target template, by with corresponded in live video stream target carry out
Characteristic matching and then progress similitude judgement.
Further, with surf method, call the Feature Detector interface in openCV interested to find
Point realizes the detection process of mobile object using Surf Feature Detector and its function detect.
Further, it in step S5, is randomly selected within the scope of different sample areas under different scale images first
Characteristic point information uses Flann Based Matcher interface and function FLANN later, and realization rapidly and efficiently matches, will be high
After dimensional feature information carries out dimensionality reduction, specific apparent model is established in corresponding compression domain.
Further, when initial frame, sampling obtain the sample data of several target and backgrounds, then to its into
Row multi-scale transform, then dimensionality reduction is carried out to multi-scale image feature by sparseness measuring matrix, then pass through the feature after dimensionality reduction
It goes to train Naive Bayes Classifier.
Further, in step S9, position ring control the following steps are included:
S1: the focal length of camera is determined, it is assumed that the target that a width is w.It is d that this target, which is placed on apart from camera,
Position and measure the pixel wide p of object, obtain camera focus formula:
F=(p × d)/w
S2: the horizontal displacement of unmanned plane and target is calculated by the location of pixels of target in the picture, if target exists
The location of pixels of the plane of delineation is (u, v), then the horizontal displacement (x, y) between target and unmanned plane indicates are as follows:
Z is that unmanned plane height f is camera focus, and β is the pitch angle of video camera, and α is image two o'clock line and camera optical axis
Angulation, (u0, v0) are image center pixel coordinate.
S3: the difference of unmanned plane and the real standard distance of mobile target is inputted as the PID of unmanned plane position control
Parameter, unmanned plane is controlled by position ring and carries out position adjustment, for the position control of unmanned plane, it is assumed that L is as target position
With the difference of current moment position, then control amount φ is exportedk: meet following PID control relationship with L:
S4: by the ideal control amount φ of unmanned plane position controlkDeparture φ is set as with the difference of actual measured valueDeviation k, described
Departure and final control export φfinalkMeet following PID control relationship:
It crosses a sparseness measuring matrix and dimensionality reduction is carried out to multi-scale image feature, then go to train by the feature after dimensionality reduction
Tracing problem is converted to the duality classification problem using Naive Bayes Classifier by Naive Bayes Classifier.
It is an object of that present invention to provide a set of targets based on automatic control algorithm and combined high precision, common-path interference to chase after
The unmanned plane method for real time tracking of track method, can overcome the deficiencies of Normal visual sensor position is fixed, field range is small, can
The very effective field range for expanding visual sensor.It is equipped with the rotor type unmanned plane of visual sensor, it can be right in real time
It ground and is monitored in the air, can also be monitored and be tracked to target is moved, many monitor tasks can be efficiently accomplished.
The utility model has the advantages that
1, the present invention will be tracked the various dimensions difference between target and unmanned plane and converted by multi-scale image Feature Dimension Reduction
For the difference of unmanned plane and the real standard distance of target position, monocycle position ring PID controller is inputted, is realized to mobile target
Real-time tracking, realize the data reduction of target following.
2, the present invention is based on openCV to establish target template, extracts feature vector, improves tracking precision and enhances anti-interference
Property, the influence that complex background tracks it is reduced, the calculation amount of data is reduced using compressed sensing algorithm, accelerates target tracking speed
Degree.
3, in such a way that embedded hardware and image procossing combine, data transmit-receive is high-efficient, and transmits and stablize, convenient for number
According to observation and analysis.
Detailed description of the invention
Fig. 1 is overall system frame diagram of the invention.
Fig. 2 is moving Object Detection algorithm flow chart.
Fig. 3 is compressed sensing algorithm model.
Fig. 4 is that aircraft detects controller design frame.
Specific embodiment
In order to be easy to understand the technical means, the creative features, the aims and the efficiencies achieved by the present invention, tie below
Conjunction is specifically illustrating, and the present invention is further explained.
Embodiment 1
Overall framework figure is opened as shown in Figure 1, taken off first by remote control control unmanned plane and reach tracking target proximity
After tracking mode, unmanned plane, which obtains ground image and passed back by figure, passes to the end PC, and PC termination is based on openCV after receiving image
Characteristic point detection and matching are carried out to sequence of video images, clarification of objective vector to be detected is extracted, by multi-scale transform
Afterwards, dimensionality reduction is carried out to multi-scale image feature using compressive sensing theory, the simple shellfish of training is then gone by the feature after dimensionality reduction
This classifier of leaf passes through the picture of target in the picture Naive Bayes Classifier obtains tracking target window after being classified after
Vegetarian refreshments coordinate and actual height distance are converted to the horizontal actual displacement of the two in vertical direction, by unmanned plane and mobile mesh
The difference of target real standard distance as unmanned plane position control PID input parameter, unmanned plane by position ring control into
Line position sets adjustment, realizes the tracking to mobile target.
The concrete operation step of unmanned plane Real Time Compression tracking based on OpenCV is as follows, moving Object Detection process
Distinguish with compressed sensing algorithm model as shown in Figure 2 and Figure 3.
Specific steps are as follows:
Step S1: unmanned plane takes off, and starts to acquire image, passback video data to the end PC；
Step S2: after selection unmanned plane tracking target, picture frame is converted by video；
Step S3: characteristic point detection and matching are carried out to sequence of video images based on openCV, extract target to be detected
Feature vector；
Step S4: extracting the sample data of image sequence background and target, carries out initial frame processing；
Step S5: feature vector is compressed using sparseness measuring matrix, is converted；
Step S6: during tracking, classifier is trained using treated feature vector as training set, later
Each frame image of input is all trained using the trained classifier of previous frame, show that target window realizes tracking；
Step S7: after obtaining tracking target, the position coordinates of target are calculated；
Step S8: according to the position coordinates of tracking target, the two is calculated in vertical direction in conjunction with unmanned plane altitude information
On horizontal displacement；
Step S9: using the difference of unmanned plane and the real standard distance of mobile target as the PID of unmanned plane position control
Parameter is inputted, unmanned plane is controlled by position ring and carries out position adjustment, realizes the tracking to mobile target.
It should be pointed out that characteristic point detection and matching refer in the unmanned plane Real Time Compression tracking based on OpenCV
By calling the Fast Feature Detector function setup detection threshold value of opencv to extract the angle point face of target to be detected
Color, Texture eigenvalue, establish target template, carry out and then with target progress characteristic matching is corresponded in live video stream similar
Property judgement, detailed process is as shown in Figure 2.
It should be pointed out that compressive sensing theory refers to not in the unmanned plane Real Time Compression tracking based on OpenCV
The characteristic point information under different scale images is randomly selected within the scope of same sample areas；Flann Based is used later
Matcher interface and function FLANN, realization rapidly and efficiently match, and after high dimensional feature information is carried out dimensionality reduction, press accordingly
Specific apparent model is established in contracting domain.
It should be pointed out that Real Time Compression perception tracking refers in the unmanned plane Real Time Compression tracking based on OpenCV
Nearest neighbor search is carried out to high dimensional feature and large data sets by Flann function first, dimensionality reduction is carried out after Rapid matching to it, becomes
It changes, then classifier is trained using treated feature vector as training set, each frame image inputted later is all
It is trained using the trained classifier of previous frame, show that target window realizes tracking.
It should be pointed out that in the unmanned plane Real Time Compression tracking based on OpenCV when initial frame, sampling
The sample data of several target and backgrounds is obtained, multi-scale transform is then carried out to it, then pass through a sparseness measuring matrix
Dimensionality reduction is carried out to multi-scale image feature, then goes to train Naive Bayes Classifier, sparse dimension reduction by the feature after dimensionality reduction
Process is as shown in Figure 3.
It should be pointed out that passing through Naive Bayes Classifier in the unmanned plane Real Time Compression tracking based on OpenCV
After obtaining tracking target window after being classified, converted by target pixel coordinate in the picture and actual height distance
At the horizontal actual displacement of the two in vertical direction, using the difference of unmanned plane and the real standard distance of mobile target as nothing
The PID of man-machine position control inputs parameter, unmanned plane carries out position adjustment by position ring control, realize to movement target with
Track, process are as shown in Figure 4.
Basic principles and main features and advantages of the present invention of the invention have been shown and described above, the present invention is not by upper
The limitation of embodiment is stated, the above embodiments and description only illustrate the principle of the present invention, is not departing from the present invention
Under the premise of spirit and scope, various changes and improvements may be made to the invention, these changes and improvements both fall within claimed
In the scope of the invention, the scope of the present invention is defined by the appended claims and its equivalents.
Claims (6)
1. a kind of unmanned plane Real Time Compression tracking based on OpenCV, which is characterized in that the described method includes:
Step S1: unmanned plane takes off, and starts to acquire image, passback video data to the end PC；
Step S2: after selection unmanned plane tracking target, picture frame is converted by video；
Step S3: characteristic point detection and matching are carried out to sequence of video images based on openCV, extract the spy of target to be detected
Levy vector；
Step S4: extracting the sample data of image sequence background and target, carries out initial frame processing；
Step S5: feature vector is compressed using sparseness measuring matrix, is converted；
Step S6: during tracking, classifier is trained using treated feature vector as training set, is inputted later
Each frame image be all trained using the trained classifier of previous frame, obtain target window realize tracking；
Step S7: after obtaining tracking target, the position coordinates of target are calculated；
Step S8: it according to the position coordinates of tracking target, both is calculated in vertical direction in conjunction with unmanned plane altitude information
Horizontal displacement；
Step S9: the difference of unmanned plane and the real standard distance of mobile target is inputted as the PID of unmanned plane position control
Parameter, unmanned plane are controlled by position ring and carry out position adjustment, realize the tracking to mobile target.
2. the unmanned plane Real Time Compression tracking according to claim 1 based on OpenCV, which is characterized in that step S3
In, call the Fast Feature Detector function setup detection threshold value of openCV extract target to be detected angle point color,
Texture eigenvalue establishes target template, carries out similitude and then with target progress characteristic matching is corresponded in live video stream
Judgement.
3. the unmanned plane Real Time Compression tracking according to claim 2 based on OpenCV, which is characterized in that use
Surf method calls the Feature Detector interface in openCV to find point-of-interest, uses SurfFeature
Detector and its function detect realizes the detection process of mobile object.
4. the unmanned plane Real Time Compression tracking according to claim 3 based on OpenCV, which is characterized in that step S5
In, the characteristic point information under different scale images is randomly selected within the scope of different sample areas first, uses Flann later
Based Matcher interface and function FLANN, realization rapidly and efficiently match, after high dimensional feature information is carried out dimensionality reduction, in phase
Specific apparent model is established in the compression domain answered.
5. the unmanned plane Real Time Compression tracking according to claim 1 based on OpenCV, which is characterized in that step S5
In, when initial frame, sampling obtains the sample data of several target and backgrounds, multi-scale transform then is carried out to it,
Dimensionality reduction is carried out to multi-scale image feature by sparseness measuring matrix again, the simple pattra leaves of training is then gone by the feature after dimensionality reduction
This classifier.
6. the unmanned plane Real Time Compression tracking according to claim 1 based on OpenCV, which is characterized in that step S9
In, position ring control the following steps are included:
S1: the focal length of camera is determined, it is assumed that the target that a width is w.This target is placed on to the position for being d apart from camera
The pixel wide p for setting and measuring object, obtains camera focus formula:
F=(p × d)/w
S2: the horizontal displacement of unmanned plane and target is calculated by the location of pixels of target in the picture, if target is in image
The location of pixels of plane is (u, v), then the horizontal displacement (x, y) between target and unmanned plane indicates are as follows:
It is camera focus that Z, which is unmanned plane height f, and β is the pitch angle of video camera, α be image two o'clock line and camera optical axis at
Angle, (u0, v0) be image center pixel coordinate.
S3: inputting parameter as the PID of unmanned plane position control for the difference of unmanned plane and the real standard distance of mobile target,
Unmanned plane is controlled by position ring and carries out position adjustment, for the position control of unmanned plane, it is assumed that L is as target position and now
The difference for carving position, then export control amount φk: meet following PID control relationship with L:
S4: by the ideal control amount φ of unmanned plane position controlkDeparture φ is set as with the difference of actual measured valueDeviation k, the deviation
Amount and final control export φfinalkMeet following PID control relationship:
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811063858.7A CN109445453A (en) | 2018-09-12 | 2018-09-12 | A kind of unmanned plane Real Time Compression tracking based on OpenCV |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811063858.7A CN109445453A (en) | 2018-09-12 | 2018-09-12 | A kind of unmanned plane Real Time Compression tracking based on OpenCV |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109445453A true CN109445453A (en) | 2019-03-08 |
Family
ID=65532531
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811063858.7A Pending CN109445453A (en) | 2018-09-12 | 2018-09-12 | A kind of unmanned plane Real Time Compression tracking based on OpenCV |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109445453A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110244756A (en) * | 2019-04-29 | 2019-09-17 | 福州大学 | Unmanned plane fast track collaborative obstacle avoidance method |
| CN110262529A (en) * | 2019-06-13 | 2019-09-20 | 桂林电子科技大学 | A kind of monitoring unmanned method and system based on convolutional neural networks |
| CN110298866A (en) * | 2019-06-06 | 2019-10-01 | 武汉易科空间信息技术股份有限公司 | Ground object tracking and system based on unmanned plane image technology |
| CN111311641A (en) * | 2020-02-25 | 2020-06-19 | 重庆邮电大学 | Target tracking control method for unmanned aerial vehicle |
| CN111476116A (en) * | 2020-03-24 | 2020-07-31 | 南京新一代人工智能研究院有限公司 | Rotor unmanned aerial vehicle system for vehicle detection and tracking and detection and tracking method |
| CN113079342A (en) * | 2020-01-03 | 2021-07-06 | 深圳市春盛海科技有限公司 | Target tracking method and system based on high-resolution image device |
| CN113156991A (en) * | 2021-02-05 | 2021-07-23 | 北京林业大学 | Flight evaluation system and method based on small multi-rotor aircraft |
| CN113763426A (en) * | 2021-08-30 | 2021-12-07 | 北京理工大学 | Reliable target tracking method in rapid intersection process |
| CN113848979A (en) * | 2021-10-12 | 2021-12-28 | 苏州大学 | Feedforward compensation PID control-based unmanned aerial vehicle complex dynamic target tracking method |
| CN114184086A (en) * | 2021-12-13 | 2022-03-15 | 绵阳久强智能装备有限公司 | Photoelectric tracking image alignment method for anti-sniper robot |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103544483A (en) * | 2013-10-25 | 2014-01-29 | 合肥工业大学 | United target tracking method based on local sparse representation and system thereof |
| CN103679754A (en) * | 2013-12-20 | 2014-03-26 | 河北汉光重工有限责任公司 | Real-time target searching and tracking technique |
| CN104156978A (en) * | 2014-07-04 | 2014-11-19 | 合肥工业大学 | Multi-target dynamic following method based on balloon platform |
| CN105389593A (en) * | 2015-11-16 | 2016-03-09 | 上海交通大学 | Image object recognition method based on SURF |
| WO2016131300A1 (en) * | 2015-07-22 | 2016-08-25 | 中兴通讯股份有限公司 | Adaptive cross-camera cross-target tracking method and system |
| CN106447680A (en) * | 2016-11-23 | 2017-02-22 | 湖南华诺星空电子技术有限公司 | Method for radar and vision fused target detecting and tracking in dynamic background environment |
| CN106683119A (en) * | 2017-01-09 | 2017-05-17 | 河北工业大学 | Moving vehicle detecting method based on aerially photographed video images |
| CN106774436A (en) * | 2017-02-27 | 2017-05-31 | 南京航空航天大学 | The control system and method for the rotor wing unmanned aerial vehicle tenacious tracking target of view-based access control model |
| CN107316030A (en) * | 2017-07-04 | 2017-11-03 | 西北工业大学深圳研究院 | Unmanned plane is to terrain vehicle automatic detection and sorting technique |
| CN107798691A (en) * | 2017-08-30 | 2018-03-13 | 西北工业大学 | A kind of unmanned plane independent landing terrestrial reference real-time detecting and tracking method of view-based access control model |
| CN107918772A (en) * | 2017-12-10 | 2018-04-17 | 北京工业大学 | Method for tracking target based on compressive sensing theory and gcForest |
| CN108510521A (en) * | 2018-02-27 | 2018-09-07 | 南京邮电大学 | A kind of dimension self-adaption method for tracking target of multiple features fusion |
-
2018
- 2018-09-12 CN CN201811063858.7A patent/CN109445453A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103544483A (en) * | 2013-10-25 | 2014-01-29 | 合肥工业大学 | United target tracking method based on local sparse representation and system thereof |
| CN103679754A (en) * | 2013-12-20 | 2014-03-26 | 河北汉光重工有限责任公司 | Real-time target searching and tracking technique |
| CN104156978A (en) * | 2014-07-04 | 2014-11-19 | 合肥工业大学 | Multi-target dynamic following method based on balloon platform |
| WO2016131300A1 (en) * | 2015-07-22 | 2016-08-25 | 中兴通讯股份有限公司 | Adaptive cross-camera cross-target tracking method and system |
| CN105389593A (en) * | 2015-11-16 | 2016-03-09 | 上海交通大学 | Image object recognition method based on SURF |
| CN106447680A (en) * | 2016-11-23 | 2017-02-22 | 湖南华诺星空电子技术有限公司 | Method for radar and vision fused target detecting and tracking in dynamic background environment |
| CN106683119A (en) * | 2017-01-09 | 2017-05-17 | 河北工业大学 | Moving vehicle detecting method based on aerially photographed video images |
| CN106774436A (en) * | 2017-02-27 | 2017-05-31 | 南京航空航天大学 | The control system and method for the rotor wing unmanned aerial vehicle tenacious tracking target of view-based access control model |
| CN107316030A (en) * | 2017-07-04 | 2017-11-03 | 西北工业大学深圳研究院 | Unmanned plane is to terrain vehicle automatic detection and sorting technique |
| CN107798691A (en) * | 2017-08-30 | 2018-03-13 | 西北工业大学 | A kind of unmanned plane independent landing terrestrial reference real-time detecting and tracking method of view-based access control model |
| CN107918772A (en) * | 2017-12-10 | 2018-04-17 | 北京工业大学 | Method for tracking target based on compressive sensing theory and gcForest |
| CN108510521A (en) * | 2018-02-27 | 2018-09-07 | 南京邮电大学 | A kind of dimension self-adaption method for tracking target of multiple features fusion |
Non-Patent Citations (1)
| Title |
|---|
| 尤智，刘惠义: "基于FAST-SURF的移动端实时特征检测匹配算法", 《信息技术》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110244756B (en) * | 2019-04-29 | 2021-09-24 | 福州大学 | Unmanned aerial vehicle rapid tracking cooperative obstacle avoidance method |
| CN110244756A (en) * | 2019-04-29 | 2019-09-17 | 福州大学 | Unmanned plane fast track collaborative obstacle avoidance method |
| CN110298866A (en) * | 2019-06-06 | 2019-10-01 | 武汉易科空间信息技术股份有限公司 | Ground object tracking and system based on unmanned plane image technology |
| CN110262529B (en) * | 2019-06-13 | 2022-06-03 | 桂林电子科技大学 | Unmanned aerial vehicle monitoring method and system based on convolutional neural network |
| CN110262529A (en) * | 2019-06-13 | 2019-09-20 | 桂林电子科技大学 | A kind of monitoring unmanned method and system based on convolutional neural networks |
| CN113079342A (en) * | 2020-01-03 | 2021-07-06 | 深圳市春盛海科技有限公司 | Target tracking method and system based on high-resolution image device |
| CN111311641A (en) * | 2020-02-25 | 2020-06-19 | 重庆邮电大学 | Target tracking control method for unmanned aerial vehicle |
| CN111311641B (en) * | 2020-02-25 | 2023-06-09 | 重庆邮电大学 | Unmanned aerial vehicle target tracking control method |
| CN111476116A (en) * | 2020-03-24 | 2020-07-31 | 南京新一代人工智能研究院有限公司 | Rotor unmanned aerial vehicle system for vehicle detection and tracking and detection and tracking method |
| CN113156991A (en) * | 2021-02-05 | 2021-07-23 | 北京林业大学 | Flight evaluation system and method based on small multi-rotor aircraft |
| CN113156991B (en) * | 2021-02-05 | 2023-02-17 | 北京林业大学 | Flight evaluation system and method based on small multi-rotor aircraft |
| CN113763426A (en) * | 2021-08-30 | 2021-12-07 | 北京理工大学 | Reliable target tracking method in rapid intersection process |
| CN113848979B (en) * | 2021-10-12 | 2023-01-17 | 苏州大学 | Feedforward compensation PID control-based unmanned aerial vehicle complex dynamic target tracking method |
| CN113848979A (en) * | 2021-10-12 | 2021-12-28 | 苏州大学 | Feedforward compensation PID control-based unmanned aerial vehicle complex dynamic target tracking method |
| CN114184086A (en) * | 2021-12-13 | 2022-03-15 | 绵阳久强智能装备有限公司 | Photoelectric tracking image alignment method for anti-sniper robot |
| CN114184086B (en) * | 2021-12-13 | 2023-10-03 | 绵阳久强智能装备有限公司 | Photoelectric tracking image alignment method for anti-sniper robot |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109445453A (en) | A kind of unmanned plane Real Time Compression tracking based on OpenCV | |
| CA3100569C (en) | Ship identity recognition method base on fusion of ais data and video data | |
| CN113034548B (en) | Multi-target tracking method and system suitable for embedded terminal | |
| CN109887040B (en) | Moving target active sensing method and system for video monitoring | |
| CN104217428B (en) | A kind of fusion feature matching and the video monitoring multi-object tracking method of data correlation | |
| CN102819847B (en) | Based on the movement locus extracting method of PTZ dollying head | |
| Zhang et al. | Joint human detection and head pose estimation via multistream networks for RGB-D videos | |
| CN109949361A (en) | A kind of rotor wing unmanned aerial vehicle Attitude estimation method based on monocular vision positioning | |
| CN110458025B (en) | Target identification and positioning method based on binocular camera | |
| CN102447835A (en) | Non-blind-area multi-target cooperative tracking method and system | |
| CN102214291A (en) | Method for quickly and accurately detecting and tracking human face based on video sequence | |
| CN102542289A (en) | Pedestrian volume statistical method based on plurality of Gaussian counting models | |
| CN109448326B (en) | Geological disaster intelligent group defense monitoring system based on rapid image recognition | |
| CN110414340A (en) | A kind of ship identification method in ship lock monitoring system | |
| Momeni-k et al. | Height estimation from a single camera view | |
| CN105678805B (en) | Object detection system of taking photo by plane based on ARM platform | |
| CN112785564B (en) | Pedestrian detection tracking system and method based on mechanical arm | |
| CN105118073A (en) | Human body head target identification method based on Xtion camera | |
| Li et al. | Traffic police gesture recognition for autonomous driving | |
| CN105809719A (en) | Object tracking method based on pixel multi-coding-table matching | |
| Harahap et al. | Face tracking with camshift algorithm for detecting student movement in a class | |
| CN201063764Y (en) | Intelligentized object detail capture device in video monitoring system | |
| Yang et al. | A novel object detection and localization approach via combining vision with LiDAR sensor | |
| Lu et al. | Research on target detection and tracking system of rescue robot | |
| Li et al. | A loop closure detection method based on semantic segmentation and convolutional neural network |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190308 |