CN107862240A - A kind of face tracking methods of multi-cam collaboration - Google Patents

A kind of face tracking methods of multi-cam collaboration Download PDF

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Publication number
CN107862240A
CN107862240A CN201710846556.6A CN201710846556A CN107862240A CN 107862240 A CN107862240 A CN 107862240A CN 201710846556 A CN201710846556 A CN 201710846556A CN 107862240 A CN107862240 A CN 107862240A
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face
camera
interest
area
quality
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CN107862240B (en
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刘意
文武
林湘云
陈婷
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Guangdong Kejian Detection Engineering Technology Co ltd
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Guangdong Science Testing Engineering Technology Co Ltd
Shenzhen Pulse Intelligent Technology Co Ltd
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    • 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
    • G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]

Abstract

The present invention relates to technical field of face recognition, more specifically, discloses a kind of method of the face tracking of multi-cam collaboration.This method carries out face tracking using more shootings, and the target person face images such as multiple different illumination, different postures, fuzziness difference can be obtained in the area-of-interest of monitoring scene.By setting certain weight, quality evaluation is carried out to the facial image of multiple different qualities, selects out an optimal facial image of quality.The efficiency of man face image acquiring is effectively improved by this scheme, substantially increases the discrimination of face, there is very big practical value.

Description

A kind of face tracking methods of multi-cam collaboration
Technical field
The present invention relates to technical field of face recognition, more specifically to a kind of face tracking methods.
Background technology
In existing face recognition technology, realized respectively and face detected, to the face that detects with Track, face characteristic extraction is carried out to given image or video sequence and is compared with face database data so as to be carried out to user Identification, but conventional face's identifying system is easily influenceed by the various external conditions such as illumination, beard, glasses, hair style, expression, makes knowledge Rate does not reduce.Therefore, system availability is not strong.And the recognition of face Robust Algorithms of adaptive illumination variation can be in certain journey Face identification rate is effectively improved on degree, but still still there is limitation, it is impossible to efficiently solves the influence of various scene changes. In recognition of face, the discrimination of better quality facial image also can be higher, therefore, illumination estimation, Attitude estimation and fuzzy The Quality estimation of the facial images such as estimation is the most important thing.
The content of the invention
The present invention is in order to overcome above mentioned problem, there is provided a kind of method of the face tracking of multi-cam collaboration, to improve The efficiency and utilization rate of man face image acquiring, so as to improve the discrimination of face.
In order to realize above mentioned problem, present invention employs following technical proposal：
A kind of method of the face tracking of multi-cam collaboration, its specific steps include,
S1. area-of-interest delimited according to monitoring scene；
S2. multiple cameras are set in area-of-interest, distribution camera enables to cover area-of-interest；
S3. it is just right after any one camera detects a more complete face when personnel enter area-of-interest This face is tracked using improved TLD track algorithms；
S4. after target face leaves from a certain camera picture, it is laid out according to camera in area-of-interest non-thread Property graph structure, calculate camera field of view registration and camera in facial image similarity, determine graph structure search for Weight；
S5. the camera according to side right weight from big to small, carries out Feature Points Matching to face and target face successively, until Untill the match is successful, if one circulates, matching is all unsuccessful, then illustrates that this person has left area-of-interest；
S6. Quality estimation is carried out by picture quality optimal policy to all target facial images traced into, according to matter Amount fraction selects out an optimal facial image of quality and is used for follow-up recognition of face.
Preferably, the improved TLD tracing algorithms in the step S3 detect a whole person in any camera After face, this target face is constantly learnt, positive and negative samples storehouse is constantly updated, is detected for target location in subsequent frame.
Preferably, in the step S4 calculate camera field of view registration in camera facial image it is similar Degree, is according to formula：Wi=α Ri+βSi, wherein 0≤α, β≤1, alpha+beta=1.
Preferably, the mass fraction in the step S6 is to image blur estimation, illumination estimation, Attitude estimation, glasses Detection judges that five aspects give certain weight Wi respectively with form, is according to formula：scorei=w1fi+w2li+w3pi+w4gi+ w5mi。
Compared with prior art, the device have the advantages that：
Face tracking is carried out using more shootings, multiple different illumination can be obtained in the area-of-interest of monitoring scene, The target person face image such as different postures, fuzziness difference.By setting certain weight, the facial image of multiple different qualities is entered Row quality evaluation, select out an optimal facial image of quality.The effect of man face image acquiring is effectively improved by this scheme Rate, the discrimination of face is substantially increased, there is very big practical value.
Brief description of the drawings
Fig. 1 is area-of-interest and camera position schematic diagram in embodiments of the invention；
Fig. 2 is TLD technical work principle figures；
Fig. 3 is Quality estimation flow chart in the embodiment of the present invention.
Embodiment
The present invention is further described with reference to the accompanying drawings and detailed description：
A kind of method of the face tracking of multi-cam collaboration, with reference to figure 2,1~9 is camera in figure, and A is region of interest Domain, B are monitor area, then its specific steps includes,
S1. area-of-interest A delimited according to monitoring scene B；
S2. area-of-interest sets 5 cameras in the present embodiment, and distribution camera enables to cover region of interest Domain；
S3. such as Fig. 2, when personnel enter area-of-interest, it is assumed that detect a more complete face by camera 1 Afterwards, just this face is tracked using improved TLD track algorithms；
S4. after target face leaves from a certain camera picture, it is laid out according to camera in area-of-interest non-thread Property graph structure, calculate camera field of view registration and camera in facial image similarity, determine graph structure search for Weight；
S5. the camera according to side right weight from big to small, carries out Feature Points Matching to face and target face successively, until Untill the match is successful, if one circulates, matching is all unsuccessful, then illustrates that this person has left area-of-interest；
S6. Quality estimation is carried out by picture quality optimal policy to all target facial images traced into, according to matter Amount fraction selects out an optimal facial image of quality and is used for follow-up recognition of face.
In the present embodiment, with reference to the fundamental diagram that figure 2 is TLD technologies, the improved TLD trackings in the step S3 After algorithm detects a complete face in any camera, this target face is constantly learnt, constantly updated positive and negative Sample Storehouse, detected for target location in subsequent frame.TLD technologies include tracker, learner, detector, integrator and regarded Frequency frame.The movable information that tracker is changed using frame to frame tracks target, and learner is assessed detector, and corrigendum avoids sending out Raw same mistake.Frame of video is for gathering face characteristic, and integrator is that face is compared with target face characteristic.With Track device mainly applies pyramid optical flow method, and learner is to complete online real-time learning by random forests algorithm.With reference to reality Border scene, is improved and optimizes to Face tracking algorithm, and the robustness for improving Face tracking algorithm is very crucial, to follow-up The Quality estimation of facial image has a major impact with recognition of face.
Preferably, in the step S4 calculate camera field of view registration in camera facial image it is similar Degree, is according to formula：Wi=α Ri+βSi, wherein 0≤α, β≤1, alpha+beta=1, Wi is the weight of each edge in graph structure, RiTo take the photograph As the registration of head field of view, SiFor the similarity of facial image in camera.It is laid out according to camera in area-of-interest Non-linear graph structure, calculate camera field of view registration and camera in facial image similarity, it is determined that figure knot The weight of structure search.According to the weight between each two camera being each edge, it is possible to preferably solve the association of multi-cam The same sex.
Preferably, with reference to figure 3, the mass fraction in the step S6 is that image blur estimation, illumination estimation, posture are estimated Meter, Glasses detection and form judge that five aspects give certain weight Wi respectively, are according to formula：
scorei=w1fi+w2li+w3pi+w4gi+w5mi
Wherein, scoreiFor mass fraction, fiFor image blur estimation, liFor illumination estimation, piFor Attitude estimation, giFor eye Microscopy is surveyed, miJudge for form.
Embodiments of the invention are the foregoing is only, are not intended to limit the scope of the invention, it is every to utilize this hair The equivalent structure or equivalent flow conversion that bright specification and accompanying drawing content are made, or directly or indirectly it is used in other related skills Art field, is included within the scope of the present invention.

Claims (4)

  1. A kind of 1. method of the face tracking of multi-cam collaboration, it is characterised in that its specific steps includes,
    S1. area-of-interest delimited according to monitoring scene；
    S2. multiple cameras are set in area-of-interest, distribution camera enables to cover area-of-interest；
    S3. when personnel enter area-of-interest, after any one camera detects a more complete face, just to this person Face is tracked using improved TLD track algorithms；
    S4. after target face leaves from a certain camera picture, the non-linear figure according to camera layout in area-of-interest Structure, the similarity of facial image in the registration and camera of camera field of view is calculated, determine the power of graph structure search Weight；
    S5. the camera according to side right weight from big to small, carries out Feature Points Matching to face and target face successively, until matching Untill success, if one circulates, matching is all unsuccessful, then illustrates that this person has left area-of-interest；
    S6. Quality estimation is carried out by picture quality optimal policy to all target facial images traced into, according to quality point Number selects out an optimal facial image of quality and is used for follow-up recognition of face.
  2. A kind of 2. method of the face tracking of multi-cam collaboration according to claim 1, it is characterised in that the step After improved TLD tracing algorithms in S3 detect a complete face in any camera, this target face is carried out not Disconnected study, constantly updates positive and negative samples storehouse, is detected for target location in subsequent frame.
  3. A kind of 3. method of the face tracking of multi-cam collaboration according to claim 1, it is characterised in that the step The similarity of facial image in the registration and camera of camera field of view is calculated in S4, is according to formula：Wi=α Ri+β Si, wherein 0≤α, β≤1, alpha+beta=1.
  4. A kind of 4. method of the face tracking of multi-cam collaboration according to claim 1, it is characterised in that the step Mass fraction in S6 is to judge image blur estimation, illumination estimation, Attitude estimation, Glasses detection and form five aspects point Certain weight Wi is not given, is according to formula：
    scorei=w1fi+w2li+w3pi+w4gi+w5mi。
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CN108764047A (en) * 2018-04-27 2018-11-06 深圳市商汤科技有限公司 Group's emotion-directed behavior analysis method and device, electronic equipment, medium, product
CN109934115A (en) * 2019-02-18 2019-06-25 苏州市科远软件技术开发有限公司 Construction method, face identification method and the electronic equipment of human face recognition model
CN110188722A (en) * 2019-06-05 2019-08-30 福建深视智能科技有限公司 A kind of method and terminal of local recognition of face image duplicate removal
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CN111179489A (en) * 2018-11-29 2020-05-19 广东网深锐识科技有限公司 Dynamic portrait recognition control access control and dynamic portrait recognition control method
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CN112509264A (en) * 2020-11-19 2021-03-16 深圳市欧瑞博科技股份有限公司 Abnormal intrusion intelligent shooting method and device, electronic equipment and storage medium
CN114093004A (en) * 2021-11-25 2022-02-25 成都智元汇信息技术股份有限公司 Face fusion comparison method and device based on multiple cameras

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CN114093004A (en) * 2021-11-25 2022-02-25 成都智元汇信息技术股份有限公司 Face fusion comparison method and device based on multiple cameras

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