CN106570471B - Dimension self-adaption multi-pose Face tracking based on compression track algorithm - Google Patents
Dimension self-adaption multi-pose Face tracking based on compression track algorithm Download PDFInfo
- Publication number
- CN106570471B CN106570471B CN201610947868.1A CN201610947868A CN106570471B CN 106570471 B CN106570471 B CN 106570471B CN 201610947868 A CN201610947868 A CN 201610947868A CN 106570471 B CN106570471 B CN 106570471B
- Authority
- CN
- China
- Prior art keywords
- face
- algorithm
- tracking
- target face
- target
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/161—Detection; Localisation; Normalisation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Human Computer Interaction (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a kind of dimension self-adaption multi-pose Face trackings based on compression track algorithm, coarse localization is carried out to target face using compression track algorithm, to reduce the search range of Face datection algorithm, and then improve target person face detection algorithm accuracy and real-time；Using Face datection algorithm, the accurate positionin of target face is realized, while realizing the dimension self-adaption tracking of target face；Using target person face detection algorithm, solves the problem of that target face leaves and track failure when camera lens is again introduced into；Using the continuity of target face motion process in time, the tracking continuity problem in the case of the detection failure of target person face detection algorithm is realized.The present invention can guarantee that camera carries out accurately and effectively multi-pose to target face by above method, dimension self-adaption tracking, can be widely used among video monitoring, human-computer interaction, virtual reality and various security systems such as: ATM machine monitors access control system.
Description
Technical field
The present invention relates to image steganalysis field more particularly to a kind of dimension self-adaption people based on compression track algorithm
Face track algorithm.
Background technique
In recent years, scientific research personnel achieves huge progress in face tracking technology.In the Web-based instruction, video conference, prison
Depending on requiring to track target face in real time with specific occasions such as monitoring, data transmitting and analysis.Remote teaching, view
Frequency communication, videophone, identity validation, human-computer interaction etc. are all closely bound up with face tracking.
Currently, there is much more classical Face tracking algorithm, such as Camshift track algorithm, covered based on sequence
Particle filter method, Mean shift algorithm of special Caro etc., although these algorithms can carry out accurately target face
Tracking still when the posture of target face changes, tracks and is easy failure.
To solve the problems, such as in real time effectively to track multi-pose Face, Kaihua Zhang proposes one kind and is based on
The compression of compressed sensing (compressive sensing, CS) theory tracks (Compressive Tracking, CT) algorithm.
The problem of CT algorithm very good solution carries out real-time tracking to multi-pose Face, while having operand small, tracking velocity is fast,
The advantages that strong real-time, still has that scale cannot be adaptive during tracking to target.Meanwhile when
It after target leaves camera lens, is again introduced into, tracking failure.
Summary of the invention
The technical problem to be solved by the present invention is in view of the above technical problems, propose a kind of based on compression track algorithm
Dimension self-adaption multi-pose Face tracking is effectively realized and is tracked to the dimension self-adaption of multi-pose target face.
The present invention is in order to solve the above technical problems, adopt the following technical scheme that
A kind of dimension self-adaption multi-pose Face tracking based on compression track algorithm, it is characterised in that in conjunction with face
Detection algorithm and CT algorithm carry out detecting and tracking to target face；First on video display window, target face is examined
It surveys, frame selects target face, then starts CT algorithm and carries out tracking and positioning to the target face selected by frame；On the basis of CT algorithm
On, then by the Face datection algorithm based on Adaboost and Haar feature, target face is accurately positioned.
In above-mentioned technical proposal, including following key step:
Step S1: opening camera, reads in video data stream, meanwhile, starting target person face detection algorithm is realized to video
The detection of target face in stream；
Step S2: on the basis of the target face scale detected, the generation window that a size is greater than target face is generated
Mouthful；
Step S3: CT algorithm is initialized with the window generated, constructs the Bayes classifier of the positive negative sample of CT algorithm, together
When, start CT algorithm, rough tracking is carried out to target face；
Step S4: using the tracking box of CT algorithm as the detection window of target face；
Step S5: judge whether detection window touches the boundary of video display window, if the judgment is Yes, return step
S1；Conversely, starting the Face datection algorithm based on Adaboost and Haar feature in detection window, inside detection window
Target face is detected；
Whether step S6: detecting face in judgment step S5, if the judgment is Yes, records in present frame, target person
The size and location of face, and as display box；Conversely, being bold small and position using target person in the former frame recorded
It sets, generates the display box of present frame；
Wherein, each parameter definition is as follows: detection window: the detection zone of target face, while being also the tracking of CT algorithm
Frame；Tracking box: the tracking box of CT algorithm；Display box: the tracking effect of algorithm final output.
In above-mentioned technical proposal, the detection of step S1 target face refers to identifies specific face from complex background.
In above-mentioned technical proposal, step S2 generates window on the basis of the target face center detected, and length and width are pair
1.2~1.8 times for answering target face length and width scale, to guarantee CT algorithm to the accuracy of target face tracking.
Start CT algorithm in above-mentioned technical proposal, in step S3 and carry out rough tracking to target face, including generate with
The building and update of machine calculation matrix, compression tracking Bayes classifier, specifically carry out as follows:
S31: t-th moment of step when t frame picture read in when, by the background to target face and its surrounding into
Row sampling, to get the background negative sample around several target face positive samples and target face；Then pass through one
A sparse calculation matrix carries out feature extraction to positive negative sample, then trains Bayes classifier with the feature extracted, quite
In the Bayes classifier for initializing positive negative sample, lay the groundwork for the starting of next step CT algorithm；
Step S32: when t+1 frame picture is read in, using the position of target face in t frame picture and size as
Benchmark is sampled around it, generates n detection block, then carries out feature extraction, feature extraction institute to this n detection block
The sparseness measuring matrix of use and step S1 are identical to sparseness measuring matrix involved in the detection of target face in video flowing；
Bayes classifier these features extracted to n detection block for reusing the initialization of t frame are classified, and are classified
The window of maximum ratio is tracking box；New target window is thus got.
In above-mentioned technical proposal, the generating process of the n detection block are as follows: with rectangular area position where target face
The upper left corner be that 45 positive samples using 4 pixels as radius are chosen in the center of circle；Using 8 pixels as inside radius, 25 pixels are outer
Radius chooses 50 negative samples.
In above-mentioned technical proposal, the Face datection algorithm in step S5 based on Adaboost and Haar feature includes following tool
Body step:
Step S51: face is described with Haar-Like feature, using the method for integrogram come the spy to face
The characteristic value of sign is calculated；
Step S52: being classified using Adaboost algorithm, so that the feature that can most represent face is selected, namely
These Weak Classifiers, are then combined by Haar-Like rectangular characteristic block, and then construct a strong classifier；
Step S53: the strong classifier that training obtains is connected, thus the stacking classification of one cascade structure of composition
Device.
In above-mentioned technical proposal, the above-mentioned dimension self-adaption multi-pose Face tracking based on compression track algorithm, base
The library open source OpenCV that Visual Studio 2010 and version under 7 operating system of windows are 2.4.4.
In above-mentioned technical proposal, the opening of camera, the reading of video and the formation of video frame are all based on
The library function in the library OpenCV.
In compared with the existing technology to target face tracking during, although Compression alone track algorithm can be realized colourful
State face is continuously tracked, but scale but cannot be adaptive；And simple Face datection algorithm, in the detection process, although
It can accomplish the dimension self-adaption to target face tracking, still, since Face datection algorithm cannot be guaranteed that each frame can be examined
Target face is measured, tracking picture will appear non-continuous event, and when occurring multiple faces in picture, non-targeted face pair
There is the problems such as interference in the tracking of target face.It is proposed by the present invention based on compression tracking dimension self-adaption multi-pose Face with
Track algorithm, continuous, quick, effective tracking can be carried out and based on Adaboost to multi-pose Face by combining compression track algorithm
The advantages of Face datection algorithm of learning algorithm can quickly and effectively detect face realizes and carries out ruler to multi-pose Face
Spend adaptive tracing.Coarse localization is carried out to target face using compression track algorithm, to reduce Face datection algorithm
Search range, and then improve target person face detection algorithm accuracy and real-time；Using Face datection algorithm, target face is realized
Accurate positionin, while realize target face dimension self-adaption tracking；Using target person face detection algorithm, target face is solved
When leaving camera lens and being again introduced into, the problem of tracking failure；Using the continuity of target face motion process in time, mesh is realized
Mark the tracking continuity problem in the case of the detection failure of Face datection algorithm.The present invention can guarantee camera by above method
Accurately and effectively multi-pose is carried out to target face, dimension self-adaption tracking can be widely used in video monitoring, man-machine friendship
Mutually, among virtual reality and various security systems such as: ATM machine monitors access control system.
Detailed description of the invention
Fig. 1 is the dimension self-adaption multi-pose Face tracking flow chart of the invention based on compression track algorithm.
Specific embodiment
Technical solution in order to further illustrate the present invention is described in detail this programme below in conjunction with attached drawing 1.
Dimension self-adaption multi-pose Face tracking of the invention based on compression track algorithm as shown in Figure 1, in reality
During now, first on the display window of video, target face is detected, frame selects target face, then starting pressure
Contracting track algorithm carries out tracking and positioning to the target face selected by frame.Know on the basis of compressing track algorithm, then through face
Other algorithm, accurately positions target face.For convenience of the explanation of problem, now it is defined as follows:
Detection window: the detection zone of target face；
Tracking box: the tracking box of track algorithm；
Display box: the tracking effect of algorithm final output.
In above scheme, the technology platform needed is the Visual Studio 2010 under 7 operating system of windows, with
And version is the library open source OpenCV of 2.4.4.
In above scheme, the opening of camera, the reading of video and the formation of video frame are all based on the library OpenCV
Library function.
In above scheme, face recognition algorithms in the library OpenCV that face recognition algorithms are.
In above scheme, specifically comprise the following steps:
Step S1: opening camera, reads in video data stream, meanwhile, starting target person face detection algorithm is realized to video
The detection of target face in stream.
Target Face datection refers to identifies specific face from complex background.
Step S2: on the basis of the target face detected, the window that a size is slightly larger than target face is generated.
To further illustrate the concrete methods of realizing for generating window, supplement following steps:
Step S21: it is found through experiment that, for CT algorithm during tracking to target, target face is bigger, and tracking is got over
Accurately.Therefore during generating window, for the present invention on the basis of the target face center detected, length and width are corresponding mesh
1.2~1.8 times for marking face length and width scale, to guarantee CT algorithm to the accuracy of target face tracking.
Step S3: with initialization CT algorithm is generated, constructing the Bayes classifier of the positive negative sample of CT algorithm, meanwhile, starting
CT algorithm carries out rough tracking to target face.It is as follows to compress track algorithm:
Step S31: when t frame picture is read in, being sampled by the background to target face and its surrounding, from
And get several positive samples (target face) and negative sample (background around target face).Then sparse by one
Calculation matrix positive negative sample is carried out feature extraction (i.e. realization dimensionality reduction), then with the trained Bayes's classification of the feature extracted
Device is equivalent to the Bayes classifier for initializing positive negative sample, and the starting for compression track algorithm in next step is laid the groundwork.
Step S32: when t+1 frame picture is read in, using the position of target face in t frame picture and size as
Benchmark is sampled around it, disposably generates n detection block, is then carried out feature extraction to this n detection block and (is used
Sparseness measuring matrix and step S1 in it is identical).Bayes classifier these features for reusing the initialization of t frame are divided
Class, the window for the maximum ratio classified are target window.New target window is thus got.
For the concrete methods of realizing for further illustrating compression track algorithm, following steps are supplemented:
Step S321: random measurement matrix is generated
One very typical random measurement matrix is random gaussian matrix, and matrix element meets N (0,1) distribution.But
When needing to carry out dimensionality reduction to the higher image space of dimension, this matrix is not able to satisfy actual demand in sparse degree,
A kind of very sparse mxn (m row, n column) random measurement matrix R, element r in matrix are used in CT algorithmijIt (indicates random to survey
I-th row of moment matrix, jth column)
As s=2,1-1/s=1/2, that is to say has 1/2 element in random measurement matrix be zero, and calculation amount becomes former
1/2 come；
As s=3,1-1/s=2/3, that is to say has 2/3 element in random measurement matrix be zero, and calculation amount becomes former
2/3 come；
By this matrix, calculation amount just greatly reduces.S=m/4 is had chosen in CT algorithm, matrix R's is each
Value that row only needs to calculate c element (number of sampling block that c expression generates at random, less than 4, generally 2 or 3).So
Its computation complexity is O (cn).In addition, we only need to store the nonzero element of R, so required memory space is also very
It is few.
Step S322: the building and update of compression tracking Bayes classifier
Compressing classifier employed in track algorithm is Bayes classifier, at the dimensionality reduction by random measurement matrix
After reason, corresponding characteristic value is acquired, it is assumed that each element is all independently distributed.The then classification standard of classifier H (v)
Are as follows:
Wherein, y ∈ (0,1) is classification samples label, and the value 0,1 of y respectively indicates positive sample and negative sample, viIndicate i-th
A given sample, P (vi| y=1) and P (vi| y=0) respectively indicate the probability that given sample is positive sample and negative sample.It is assumed that two
The priori conditions of class sample are identical, i.e. P (y=0)=0.5=P (y=1).It is random that Diaconis and Freedman demonstrates higher-dimension
The accidental projection of vector is nearly all Gaussian Profile.Therefore it is presumed that the conditional probability P (v in classifier H (v)i| y=1)
With P (vi| y=0) Gaussian Profile is also belonged to, and four parameters can be usedTo describe:
In formula,Positive negative sample is respectively indicated, 1 expression positive sample classifier is above designated as, is above designated as 0 expression
Negative sample classifier,iIndicate theiIt is a,WithRespectively indicating mean value isVariance isGaussian Profile
It is with mean valueVariance isGaussian Profile.The corresponding parameter for Bayes classifier updates, and has:
In formula: λ is Studying factors, and λ > 0.
Step S4: using the tracking box of CT algorithm as the detection window of target face.
Since the size of the tracking box of CT algorithm is slightly larger than target face, when target face is close to camera, according to
It can so guarantee that target face is in the inside of detection window, to guarantee scale of the target face during camera certainly
It adapts to.
Step S5: judging whether detection window touches the boundary of video window, if the judgment is Yes, return step S1；
Conversely, starting the Face datection algorithm based on Adaboost algorithm and Haar-Like feature in detection window, to detection window
Internal target face is detected.
By the judgement on boundary, target face tracking is reasonably switched with detection process, solve CT algorithm with
The tracking Problem of Failure occurred during track target face (during tracking using CT algorithm to target item, works as mesh
After mark leaves camera lens, when being again introduced into, it may appear that the problem of tracking is failed).
To further illustrate the Face datection algorithm based on Adaboost algorithm and Haar-Like feature, following step is supplemented
It is rapid:
Step S51: face is described with Haar-Like feature, using the method for integrogram come the spy to face
The characteristic value of sign is calculated.
Step S52: being classified (this is equivalent to a weak typing) using Adaboost algorithm, most can generation to select
These Weak Classifiers, are then combined by the feature (Haar-Like rectangular characteristic block) of table face, and then construct one strong
Classifier.
Step S53: the strong classifier that training obtains is connected, thus the stacking classification of one cascade structure of composition
Device can effectively improve the speed of detection by cascade mode.
Whether step S6: detecting face in judgment step S5, if the judgment is Yes, records in present frame, target face
Size and location, and as display box；Conversely, using target person face size and location in the former frame recorded,
Generate the display box of present frame.
Dimension self-adaption multi-pose Face tracking based on compression track algorithm of the invention, it is characterised in that: two
A algorithm --- the combination of compression tracking (Compressive Tracking) algorithm and Face datection algorithm, advantage obtain
It is complementary.During algorithm is realized, the Visual Studio 2010 and version being mainly utilized under 7 platform of windows are
2.4.4 algorithm is realized in the library open source OpenCV.
Due in the library OpenCV, the front face based on Adaboost algorithm and Haar-Like eigenface detection algorithm
Detection angle range substantially [- 20., 20.], when the deflection angle of face is more than this range, detection can fail, and then make
Tracking process is obtained to occur discontinuously.
In view of the main reason for detection failure is that face deflection angle is excessive, and in deflection process, face is relative to mirror
The distance of head does not occur more significantly to change, therefore, can be using the size of target face in former frame as in present frame
The size of target face.And CT algorithm has preferable robustness, the i.e. variation of posture not for the attitudes vibration of target face
Tracking effect more can be significantly influenced, thus it can be assumed that in adjacent two field pictures, target item and tracking box central point
Between relative position be constant, and then by former frame, the relative position between target item and the central point of tracking box, with
And the position of tracking box central point determines the position of target item in present frame in present frame.
To sum up, the dimension self-adaption multi-pose Face tracking proposed by the invention based on compression track algorithm, benefit
Can be real-time with compression track algorithm, quickly target face is tracked, then on the basis of compressing tracking, people is added
Face detection algorithm detects target face, to realize tracking.The algorithm, which substantially overcomes, will compress track algorithm application
During face tracking, the problem and single Face datection algorithm that scale cannot be adaptive can not rule out non-targeted people in picture
During face realizes tracking to the interference of target face tracking and single Face datection, due to not detecting target face
So that tracking discontinuous problem.It is proposed of the invention has so that the tracking of target face is simpler quickly in camera
Good expansion and practicability.
Claims (8)
1. a kind of dimension self-adaption multi-pose Face tracking based on compression track algorithm, it is characterised in that examined in conjunction with face
Method of determining and calculating and CT algorithm carry out detecting and tracking to target face；First on video display window, target face is detected,
Frame selects target face, then starts CT algorithm and carries out tracking and positioning to the target face selected by frame；On the basis of CT algorithm,
Again by the Face datection algorithm based on Adaboost and Haar feature, target face is accurately positioned；Including as follows
Key step:
Step S1: opening camera, reads in video data stream, meanwhile, starting target person face detection algorithm is realized in video flowing
The detection of target face；
Step S2: on the basis of the target face scale detected, the generation window that a size is greater than target face is generated；
Step S3: initializing CT algorithm with the window generated, constructs the Bayes classifier of the positive negative sample of CT algorithm, meanwhile, it opens
Dynamic CT algorithm, rough tracking is carried out to target face；
Step S4: using the tracking box of CT algorithm as the detection window of target face；
Step S5: judging whether detection window touches the boundary of video display window, if the judgment is Yes, return step S1；
Conversely, starting the Face datection algorithm based on Adaboost and Haar feature in detection window, to the mesh inside detection window
Mark face is detected；
Whether step S6: detecting face in judgment step S5, if the judgment is Yes, records in present frame, target face
Size and location, and as display box；Conversely, using target person face size and location in the former frame recorded, it is raw
At the display box of present frame；
Wherein, each parameter definition is as follows: detection window: the detection zone of target face, while being also the tracking box of CT algorithm；With
Track frame: the tracking box of CT algorithm；Display box: the tracking effect of algorithm final output.
2. the dimension self-adaption multi-pose Face tracking according to claim 1 based on compression track algorithm, special
Sign is that the detection of step S1 target face refers to and identifies specific face from complex background.
3. the dimension self-adaption multi-pose Face tracking according to claim 1 based on compression track algorithm, special
Sign is that step S2 generates window on the basis of the target face center detected, and length and width are corresponding target face length and width scale
1.2~1.8 times, to guarantee CT algorithm to the accuracy of target face tracking.
4. the dimension self-adaption multi-pose Face tracking according to claim 1 based on compression track algorithm, special
Sign is that starting CT algorithm in step S3 carries out rough tracking to target face, including generate random measurement matrix, compression with
The building and update of track Bayes classifier specifically carry out as follows:
S31: t-th moment of step when t frame picture is read in, is adopted by the background to target face and its surrounding
Sample, to get the background negative sample around several target face positive samples and target face；Then dilute by one
Thin calculation matrix carries out feature extraction to positive negative sample, then trains Bayes classifier with the feature extracted, and is equivalent to just
The Bayes classifier of the positive negative sample of beginningization is laid the groundwork for the starting of next step CT algorithm；
Step S32: when t+1 frame picture is read in, using the position of target face in t frame picture and size as base
Standard is sampled around it, generates n detection block, then carries out feature extraction to this n detection block, feature extraction is adopted
Sparseness measuring matrix and step S1 are identical to sparseness measuring matrix involved in the detection of target face in video flowing；Again
Classified using Bayes classifier these features extracted to n detection block that t frame initializes, is classified most
The window of large scale is tracking box；New target window is thus got.
5. the dimension self-adaption multi-pose Face tracking according to claim 4 based on compression track algorithm, special
Sign is the generating process of the n detection block are as follows: using the upper left corner of rectangular area position where target face as the center of circle, with
4 pixels are radius, choose 45 positive samples；Using 8 pixels as inside radius, 25 pixels are that outer radius chooses 50 negative samples
This.
6. the dimension self-adaption multi-pose Face tracking according to claim 1 based on compression track algorithm, special
Sign is that the Face datection algorithm in step S5 based on Adaboost and Haar feature comprises the following specific steps that:
Step S51: face is described with Haar-Like feature, using the method for integrogram come the feature to face
Characteristic value is calculated；
Step S52: being classified using Adaboost algorithm, to select the feature namely Haar- that can most represent face
These Weak Classifiers, are then combined by Like rectangular characteristic block, and then construct a strong classifier；
Step S53: the strong classifier that training obtains is connected, thus the cascade filtering of one cascade structure of composition.
7. the dimension self-adaption multi-pose Face tracking according to claim 1 based on compression track algorithm, special
The Visual Studio 2010 being based under 7 operating system of windows and version are levied as the open source OpenCV of 2.4.4
Library.
8. the dimension self-adaption multi-pose Face tracking according to claim 1 based on compression track algorithm, special
Sign is the opening of camera, the reading of video and the formation of video frame, is all based on the library function in the library OpenCV.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610947868.1A CN106570471B (en) | 2016-10-26 | 2016-10-26 | Dimension self-adaption multi-pose Face tracking based on compression track algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610947868.1A CN106570471B (en) | 2016-10-26 | 2016-10-26 | Dimension self-adaption multi-pose Face tracking based on compression track algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106570471A CN106570471A (en) | 2017-04-19 |
| CN106570471B true CN106570471B (en) | 2019-04-26 |
Family
ID=58535097
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610947868.1A Active CN106570471B (en) | 2016-10-26 | 2016-10-26 | Dimension self-adaption multi-pose Face tracking based on compression track algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106570471B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109990893B (en) * | 2017-12-29 | 2021-03-09 | 浙江宇视科技有限公司 | Photometric parameter calculation method and device and terminal equipment |
| CN108805094A (en) * | 2018-06-19 | 2018-11-13 | 合肥工业大学 | Data enhancement methods based on artificial face |
| CN109271883A (en) * | 2018-08-28 | 2019-01-25 | 武汉科技大学 | A kind of method for tracking target merging study mechanism |
| CN110705478A (en) * | 2019-09-30 | 2020-01-17 | 腾讯科技（深圳）有限公司 | Face tracking method, device, equipment and storage medium |
| CN111259907B (en) * | 2020-03-12 | 2024-03-12 | Oppo广东移动通信有限公司 | Content identification method and device and electronic equipment |
| CN113591607B (en) * | 2021-07-12 | 2023-07-04 | 辽宁科技大学 | Station intelligent epidemic situation prevention and control system and method |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103905733A (en) * | 2014-04-02 | 2014-07-02 | 哈尔滨工业大学深圳研究生院 | Method and system for conducting real-time tracking on faces by monocular camera |
-
2016
- 2016-10-26 CN CN201610947868.1A patent/CN106570471B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103905733A (en) * | 2014-04-02 | 2014-07-02 | 哈尔滨工业大学深圳研究生院 | Method and system for conducting real-time tracking on faces by monocular camera |
Non-Patent Citations (3)
| Title |
|---|
| 一种改进的实时压缩跟踪算法;钟权 等;《光电工程》;20140430;全文 |
| 基于Adaboost算法的驾驶员疲劳驾驶检测;熊池亮;《中国优秀硕士学位论文全文数据库 工程科技II辑》;20140115;第4页第2段，第17页最后一段，第18页 |
| 尺度自适应的多模型压缩跟踪算法;刘晴 等;《系统工程与电子技术》;20160430;全文 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106570471A (en) | 2017-04-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106570471B (en) | Dimension self-adaption multi-pose Face tracking based on compression track algorithm | |
| CN110147743B (en) | Real-time online pedestrian analysis and counting system and method under complex scene | |
| Ji et al. | Advances in view-invariant human motion analysis: A review | |
| Kamal et al. | A hybrid feature extraction approach for human detection, tracking and activity recognition using depth sensors | |
| Thome et al. | A real-time, multiview fall detection system: A LHMM-based approach | |
| TWI430185B (en) | Facial expression recognition systems and methods and computer program products thereof | |
| Qiao et al. | Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition | |
| Czupryński et al. | High accuracy head pose tracking survey | |
| CN102385691A (en) | Facial expression identification system, identification device and identification method | |
| Tan et al. | Real-time accurate 3d head tracking and pose estimation with consumer rgb-d cameras | |
| Núnez et al. | Real-time human body tracking based on data fusion from multiple RGB-D sensors | |
| Tian et al. | Absolute head pose estimation from overhead wide-angle cameras | |
| Ghorbel et al. | An extension of kernel learning methods using a modified log-euclidean distance for fast and accurate skeleton-based human action recognition | |
| Huang et al. | Event-based simultaneous localization and mapping: A comprehensive survey | |
| Wang et al. | A new hand gesture recognition algorithm based on joint color-depth superpixel earth mover's distance | |
| Badave et al. | Head pose estimation based robust multicamera face recognition | |
| Ko et al. | Rectified trajectory analysis based abnormal loitering detection for video surveillance | |
| Park et al. | Understanding human interactions with track and body synergies (TBS) captured from multiple views | |
| Pham et al. | Robust real-time 3d face tracking from rgbd videos under extreme pose, depth, and expression variation | |
| Peng et al. | View-invariant full-body gesture recognition via multilinear analysis of voxel data | |
| Middleton et al. | Developing a non-intrusive biometric environment | |
| CN114640807A (en) | Video-based object counting method and device, electronic equipment and storage medium | |
| Vo et al. | An effective approach for human actions recognition based on optical flow and edge features | |
| Ding et al. | Combining adaptive hierarchical depth motion maps with skeletal joints for human action recognition | |
| Zhong et al. | DynaTM-SLAM: Fast filtering of dynamic feature points and object-based localization in dynamic indoor environments |
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 |