CN113936124A - Personnel identity identification method and device based on big data collision detection - Google Patents
Personnel identity identification method and device based on big data collision detection Download PDFInfo
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Abstract
The invention discloses a personnel identity identification method based on big data collision detection, which comprises the following steps: acquiring face information and a mobile equipment address; establishing a preliminary mapping relation between the face information and the address of the mobile equipment according to the acquired face information and the address of the mobile equipment; establishing a face track and a mobile equipment address track according to the preliminary mapping relation, and calculating and acquiring a track graph between the face track and the mobile equipment address track through a Fraunhofer distance algorithm; calculating the Fraunhofer distance between the acquired face track and the address track of the mobile equipment; the invention can effectively associate personnel and equipment and trace the personnel, does not need personnel to register, can detect the person only by the personnel carrying the mobile equipment and passing through the area with the probe and the human face equipment, and associates, thereby being convenient for tracing the signal of the mobile equipment to trace the personnel at the later stage.
Description
Technical Field
The invention relates to a personnel identity recognition method and device based on big data collision detection, and belongs to the technical field of identity recognition.
Background
With the development of science and technology, the circulation of vehicles and pedestrians is rapidly increased, various illegal criminal means are improved, and in order to improve the analysis of case situations and the tracking and positioning of personnel, the force is also increased on the construction of vehicle bayonets, human face bayonets, WIFI probes and the like, and the deployment capacity is improved.
The places generate a large amount of pedestrian, vehicle and probe data every day, the data are generally single and cannot form a consistent association relationship, the person whereabouts cannot be formed in the places with single equipment, the person registration is complicated when the person registration is needed, and the data are inaccurate.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a personnel identity identification method and a personnel identity identification device based on big data collision detection, and solves the problems that the existing equipment cannot form the whereabouts of personnel, is more complicated when personnel registration is needed, and has inaccurate data.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the invention provides a personnel identity identification method based on big data collision detection, which comprises the following steps:
acquiring face information and a mobile equipment address, wherein the face information and the mobile equipment address are obtained by selecting a plurality of monitoring time points for monitoring in a pre-selected target area;
establishing a preliminary mapping relation between the face information and the address of the mobile equipment according to the acquired face information and the address of the mobile equipment;
establishing a face track and a mobile equipment address track according to the preliminary mapping relation, and calculating and acquiring a track graph between the face track and the mobile equipment address track through a Fraunhofer distance algorithm;
calculating the Fraunhofer distance between the acquired face track and the address track of the mobile equipment, comparing the Fraunhofer distance with a preset threshold, determining a real mapping relation if the Fraunhofer distance is smaller than the preset threshold, and removing the preliminary mapping relation if the face information is not matched with the address of the mobile equipment if the Fraunhofer distance is larger than or equal to the threshold;
and traversing all face tracks to obtain face information of all people and mobile equipment address fusion data.
Further, the face information is acquired through a face acquisition device, input into a deep convolutional neural network to extract face features and then acquired, and the address of the mobile device is acquired and stored through a probe, wherein the face acquisition device and the probe are arranged in a target area in advance.
Further, establishing a preliminary mapping relationship between the face information and the mobile device address according to the acquired face information and the mobile device address, including:
dividing the monitoring time points into { T1, T2, …, Tn }, n being a positive integer;
generating collision information according to the face information and the address of the mobile equipment aiming at the monitoring time T1, and recording collision times and collision time;
updating the collision information by sequentially using the face information and the mobile equipment address acquired from the monitoring time T2 to Tn;
if the collision times reach a preset threshold value, establishing a collision table corresponding to the face information and the address of the mobile equipment according to the collision information;
the collision table is updated by sequentially using the face information and the mobile device address acquired from the monitoring time { T2, …, Tn }, and the method further comprises the following steps: sorting the addresses of the mobile devices corresponding to the face information in the collision table according to the collision times;
setting a time threshold, extracting a record with the collision frequency of 1 in the collision table, and deleting the record if the interval between the collision time of the record and the current time exceeds the time threshold;
and if the monitoring time Ti (i is more than or equal to 1 and less than or equal to n) and the number of the faces in the target area is 1, establishing a preliminary mapping relation between the face information and the address of the mobile equipment.
Further, the collision information is recorded and generated once when the face information and the address of the mobile device simultaneously appear together.
Further, the mobile equipment address comprises international mobile equipment identity IMEI, bluetooth Mac and WiFi Mac.
In a second aspect, the present invention provides a personnel identification device based on big data collision detection, including:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring face information and a mobile equipment address, and the face information and the mobile equipment address are obtained by selecting a plurality of monitoring time points for monitoring in a pre-selected target area;
the mapping relation establishing unit is used for establishing a preliminary mapping relation between the face information and the address of the mobile equipment according to the acquired face information and the address of the mobile equipment;
a track relation establishing unit for establishing a face track and a mobile equipment address track according to the preliminary mapping relation, and calculating and acquiring a track graph between the face track and the mobile equipment address track through a Fraunhofer distance algorithm;
the calculating unit is used for calculating the Fraunhofer distance between the acquired face track and the address track of the mobile equipment, comparing the obtained face track and the address track of the mobile equipment with a preset threshold, determining the real mapping relation if the Fraunhofer distance is smaller than the preset threshold, and removing the preliminary mapping relation if the face information is not matched with the address of the mobile equipment if the face information is larger than or equal to the threshold;
and the traversing unit is used for traversing all the face tracks to obtain the face information of all the persons and the address fusion data of the mobile equipment.
The system further comprises a face acquisition device and a probe, wherein the face acquisition device and the probe are arranged in a target area in advance, the face acquisition device is used for acquiring the face information, and the probe is used for acquiring and storing the address of the mobile equipment.
Further, the mobile equipment address comprises international mobile equipment identity IMEI, bluetooth Mac and WiFi Mac.
In a third aspect, the invention provides a personnel identity recognition device based on big data collision detection, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate according to the instructions to perform the steps of any of the above methods.
In a fourth aspect, the invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of any of the methods described above.
Compared with the prior art, the invention has the following beneficial effects:
the invention utilizes the probe and the face recognition technology to comprehensively utilize various algorithm technologies to carry out correlation collision, can effectively correlate personnel and equipment and track the trace of the personnel, does not need personnel to register, can detect the personnel only by the personnel carrying the mobile equipment and passing through the area with the probe and the face equipment, and can correlate the personnel, thereby being convenient for tracking the signal of the mobile equipment to track the trace of the personnel in the later period.
Drawings
Fig. 1 is a flowchart of a person identification method based on big data collision detection according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1
The embodiment introduces a personnel identity identification method based on big data collision detection, which comprises the following steps:
acquiring face information and a mobile equipment address, wherein the face information and the mobile equipment address are obtained by selecting a plurality of monitoring time points for monitoring in a pre-selected target area;
establishing a preliminary mapping relation between the face information and the address of the mobile equipment according to the acquired face information and the address of the mobile equipment;
establishing a face track and a mobile equipment address track according to the preliminary mapping relation, and calculating and acquiring a track graph between the face track and the mobile equipment address track through a Fraunhofer distance algorithm;
calculating the Fraunhofer distance between the acquired face track and the address track of the mobile equipment, comparing the Fraunhofer distance with a preset threshold, determining a real mapping relation if the Fraunhofer distance is smaller than the preset threshold, and removing the preliminary mapping relation if the face information is not matched with the address of the mobile equipment if the Fraunhofer distance is larger than or equal to the threshold;
and traversing all face tracks to obtain face information of all people and mobile equipment address fusion data.
The application process of the personnel identity identification method based on big data collision detection provided by the embodiment specifically relates to the following steps:
referring to fig. 1, the whole process flow is as follows:
step one, information acquisition
And face recognition equipment and probe equipment are arranged at each bayonet and are used for face capture and equipment detection.
Selecting a plurality of monitoring time points { T1, T2, … and Tn } (n is a positive integer), shooting images of people in a target area by using a face recognition device, extracting face information from the images, acquiring a mobile device identification number in the target area by using a probe device, and storing the information.
Mobile device identification number: including the international mobile equipment identity IMEI, bluetooth Mac, WiFi Mac, etc.
The face information is characterized in that a color histogram and a gradient histogram are extracted from a head-shoulder area in an image to form a characteristic representing the face, and a face characteristic value is obtained by calculating the matching degree of the head-shoulder characteristic between frames. After the human face is aligned and calibrated based on the feature points, the human face is input to a deep convolutional neural network to extract features, and the feature dimension is 1024 dimensions. The deep convolutional neural network is composed of a residual error network, and finally generates convolutional neural network parameters for extracting the human face characteristics by using a convolutional layer composed of a multi-latitude convolutional kernel and a pooling layer and taking a cosine distance loss function as an evaluation function.
Step two, collision relation
Collision: if the face and the mobile equipment identification number appear together at the same time, the collision is recorded as a collision.
And for the monitoring time T1, generating collision information (namely face and mobile equipment identification number recording information) according to the face and mobile equipment information acquired in the last step, and recording the collision times and the collision time. And updating the collision information of the previous step by sequentially using the face and the mobile equipment identification numbers acquired from the monitoring time T2 to Tn.
And if the collision frequency reaches a threshold value set by the system, establishing a corresponding relation between the human face and the mobile equipment identification number according to the collision information.
(1) Sequentially judging whether the face obtained at the monitoring time Tj (j is more than or equal to 2 and less than or equal to n) is the face extracted in the T1 monitoring time period;
(2) if so, sequentially judging whether the mobile equipment identification number acquired by the monitoring time Tj and the current face have a collision recorded before according to the collision information, if so, correspondingly adding one to the number of collision recording times, updating the collision time according to the monitoring time Tj, and otherwise, newly adding the mobile equipment identification number to the collision information.
(3) If the detected face is not the face detected in T1, the face and the identification number of the mobile device colliding with the face are newly added to the collision information, and the number of collisions and the collision time are recorded.
Step three, collision analysis
And updating the collision table by sequentially using the faces and the mobile equipment identification numbers acquired by the monitoring time { T2, … and Tn }, and then sequencing the mobile equipment identification numbers corresponding to the faces in the collision table according to the collision times.
And step four, setting a time threshold, extracting a record with the collision frequency of 1 in the collision table, and deleting the record if the interval between the collision time of the record and the current time exceeds the time threshold.
And fifthly, if the monitoring time Ti (i is more than or equal to 1 and less than or equal to n) and the number of the faces in the target area is 1, establishing a preliminary mapping relation between the faces and the mobile equipment identification number acquired by the probe. Thus, a local mapping relation is formed;
and step six, finding out all tracks of the equipment and the personnel according to the mapping relation, and calculating track maps of the equipment and the personnel according to a Fraunhofer distance algorithm.
Device trajectory: f: [ a, b ] → v
Trajectory of the person: g [ a ', b' ] → v
Frenminbg distance: deltaF(f,g)＝inf max{d(f(α(t)),g(β(t)))}αβt∈[0,1]
The understanding of this mathematical expression is that for each pair of possible describing functions α (t) and β (t), we can always find the shortest longest distance between the person and the device during the whole movement, so this smallest distance is the frenminby distance.
A recursive method can be used to calculate the discrete frenchetdistance (frenhelt distance).
We use the form of matrix to find the upper bound of freund's break distance, that is, two continuous curves, find the key point as end point, and then couplingdistance is the distance between four points of end point.
ca (i, j) is the point i, j and the Fraunhofer's distance between these two points.
d (ui, vj) is the Euclidean distance between two points i, j.
Wherein: ca (i, j) ═ max { min (c (i-1, j), c (i-1, j-1), c (i, j-1)), d (ui, vj) }
People and equipment can reach points i and j from the above three cases. So we need to find the one distance that is the smallest of these three positions to compare with the distance of the i, j point, and then choose the largest distance as the frenzon distance of the i, j point.
ca is a matrix, n m, where n and m are the aggregate length of curves 1 and 2, i.e., the number of points on the curve. Used for storing all calculation results.
The overall calculation is such that: the calling method is to pass in the index of the last point of the two curves and then to recursively call, returning the condition that the index is calculated all the way from the last point to [0] which is the first point.
In order to enable the collected mobile equipment to correspond to the face, the coincidence degree of the face track and the address track of the mobile equipment is calculated. And calculating the Fraunhofer distance from each face track Fi in the set F to each mobile equipment address track Cj in the set C, finally obtaining a mobile equipment address track C with the minimum Fraunhofer distance, if the Fraunhofer distances of Fi and C are smaller than a set threshold value, judging that Fi and C belong to the face and the mobile equipment address of the same person, determining the face and the mobile equipment address as a real mapping relation, and if the face and the mobile equipment address are larger than or equal to the threshold value, determining that the face and the mobile equipment address are not matched, and removing the preliminary mapping relation. And traversing all face tracks to obtain the fused data of the face of all people and the address of the mobile equipment.
Example 2
This embodiment provides a personnel identification device based on big data collision detection, includes:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring face information and a mobile equipment address, and the face information and the mobile equipment address are obtained by selecting a plurality of monitoring time points for monitoring in a pre-selected target area;
the mapping relation establishing unit is used for establishing a preliminary mapping relation between the face information and the address of the mobile equipment according to the acquired face information and the address of the mobile equipment;
a track relation establishing unit for establishing a face track and a mobile equipment address track according to the preliminary mapping relation, and calculating and acquiring a track graph between the face track and the mobile equipment address track through a Fraunhofer distance algorithm;
the calculating unit is used for calculating the Fraunhofer distance between the acquired face track and the address track of the mobile equipment, comparing the obtained face track and the address track of the mobile equipment with a preset threshold, determining the real mapping relation if the Fraunhofer distance is smaller than the preset threshold, and removing the preliminary mapping relation if the face information is not matched with the address of the mobile equipment if the face information is larger than or equal to the threshold;
and the traversing unit is used for traversing all the face tracks to obtain the face information of all the persons and the address fusion data of the mobile equipment.
The system further comprises a face acquisition device and a probe, wherein the face acquisition device and the probe are arranged in a target area in advance, the face acquisition device is used for acquiring the face information, and the probe is used for acquiring and storing the address of the mobile equipment.
Further, the mobile equipment address comprises international mobile equipment identity IMEI, bluetooth Mac and WiFi Mac.
Example 3
The embodiment provides a personnel identity recognition device based on big data collision detection, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate according to the instructions to perform the steps of any of the methods described above:
acquiring face information and a mobile equipment address, wherein the face information and the mobile equipment address are obtained by selecting a plurality of monitoring time points for monitoring in a pre-selected target area;
establishing a preliminary mapping relation between the face information and the address of the mobile equipment according to the acquired face information and the address of the mobile equipment;
establishing a face track and a mobile equipment address track according to the preliminary mapping relation, and calculating and acquiring a track graph between the face track and the mobile equipment address track through a Fraunhofer distance algorithm;
calculating the Fraunhofer distance between the acquired face track and the address track of the mobile equipment, comparing the Fraunhofer distance with a preset threshold, determining a real mapping relation if the Fraunhofer distance is smaller than the preset threshold, and removing the preliminary mapping relation if the face information is not matched with the address of the mobile equipment if the Fraunhofer distance is larger than or equal to the threshold;
and traversing all face tracks to obtain face information of all people and mobile equipment address fusion data.
Example 4
The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of any of the methods described above:
acquiring face information and a mobile equipment address, wherein the face information and the mobile equipment address are obtained by selecting a plurality of monitoring time points for monitoring in a pre-selected target area;
establishing a preliminary mapping relation between the face information and the address of the mobile equipment according to the acquired face information and the address of the mobile equipment;
establishing a face track and a mobile equipment address track according to the preliminary mapping relation, and calculating and acquiring a track graph between the face track and the mobile equipment address track through a Fraunhofer distance algorithm;
calculating the Fraunhofer distance between the acquired face track and the address track of the mobile equipment, comparing the Fraunhofer distance with a preset threshold, determining a real mapping relation if the Fraunhofer distance is smaller than the preset threshold, and removing the preliminary mapping relation if the face information is not matched with the address of the mobile equipment if the Fraunhofer distance is larger than or equal to the threshold;
and traversing all face tracks to obtain face information of all people and mobile equipment address fusion data.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A personnel identity identification method based on big data collision detection is characterized by comprising the following steps:
acquiring face information and a mobile equipment address, wherein the face information and the mobile equipment address are obtained by selecting a plurality of monitoring time points for monitoring in a pre-selected target area;
establishing a preliminary mapping relation between the face information and the address of the mobile equipment according to the acquired face information and the address of the mobile equipment;
establishing a face track and a mobile equipment address track according to the preliminary mapping relation, and calculating and acquiring a track graph between the face track and the mobile equipment address track through a Fraunhofer distance algorithm;
calculating the Fraunhofer distance between the acquired face track and the address track of the mobile equipment, comparing the Fraunhofer distance with a preset threshold, determining a real mapping relation if the Fraunhofer distance is smaller than the preset threshold, and removing the preliminary mapping relation if the face information is not matched with the address of the mobile equipment if the Fraunhofer distance is larger than or equal to the threshold;
and traversing all face tracks to obtain face information of all people and mobile equipment address fusion data.
2. The personnel identity recognition method based on big data collision detection according to claim 1, characterized in that: the face information is acquired through a face acquisition device, the face information is input into a deep convolutional neural network to be acquired after face features are extracted, the address of the mobile device is acquired through a probe and stored, and the face acquisition device and the probe are arranged in a target area in advance.
3. The personnel identity recognition method based on big data collision detection according to claim 1, characterized in that: establishing a preliminary mapping relation between the face information and the address of the mobile equipment according to the acquired face information and the address of the mobile equipment, wherein the preliminary mapping relation comprises the following steps:
dividing the monitoring time points into { T1, T2, …, Tn }, n being a positive integer;
generating collision information according to the face information and the address of the mobile equipment aiming at the monitoring time T1, and recording collision times and collision time;
updating the collision information by sequentially using the face information and the mobile equipment address acquired from the monitoring time T2 to Tn;
if the collision times reach a preset threshold value, establishing a collision table corresponding to the face information and the address of the mobile equipment according to the collision information;
the collision table is updated by sequentially using the face information and the mobile device address acquired from the monitoring time { T2, …, Tn }, and the method further comprises the following steps: sorting the addresses of the mobile devices corresponding to the face information in the collision table according to the collision times;
setting a time threshold, extracting a record with the collision frequency of 1 in the collision table, and deleting the record if the interval between the collision time of the record and the current time exceeds the time threshold;
and if the monitoring time Ti (i is more than or equal to 1 and less than or equal to n) and the number of the faces in the target area is 1, establishing a preliminary mapping relation between the face information and the address of the mobile equipment.
4. The personnel identity recognition method based on big data collision detection as claimed in claim 3, wherein: and recording and generating collision information once when the face information and the address of the mobile equipment simultaneously appear together.
5. The personnel identity identification method based on big data collision detection as claimed in claim 4, wherein: the mobile equipment address comprises international mobile equipment identification codes IMEI, Bluetooth Mac and WiFi Mac.
6. A personnel identification device based on big data collision detection, its characterized in that includes:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring face information and a mobile equipment address, and the face information and the mobile equipment address are obtained by selecting a plurality of monitoring time points for monitoring in a pre-selected target area;
the mapping relation establishing unit is used for establishing a preliminary mapping relation between the face information and the address of the mobile equipment according to the acquired face information and the address of the mobile equipment;
a track relation establishing unit for establishing a face track and a mobile equipment address track according to the preliminary mapping relation, and calculating and acquiring a track graph between the face track and the mobile equipment address track through a Fraunhofer distance algorithm;
the calculating unit is used for calculating the Fraunhofer distance between the acquired face track and the address track of the mobile equipment, comparing the obtained face track and the address track of the mobile equipment with a preset threshold, determining the real mapping relation if the Fraunhofer distance is smaller than the preset threshold, and removing the preliminary mapping relation if the face information is not matched with the address of the mobile equipment if the face information is larger than or equal to the threshold;
and the traversing unit is used for traversing all the face tracks to obtain the face information of all the persons and the address fusion data of the mobile equipment.
7. The personnel identification device based on big data collision detection as claimed in claim 6, further comprising a face collecting device and a probe pre-arranged in the target area, wherein the face collecting device is used for collecting the face information, and the probe is used for acquiring and storing the mobile device address.
8. The big data collision detection-based personnel identification device according to claim 7, wherein the mobile equipment address comprises international mobile equipment identity IMEI, Bluetooth Mac, WiFi Mac.
9. The utility model provides a personnel identification device based on big data collision detects which characterized in that: comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 5.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the program when executed by a processor implements the steps of the method of any one of claims 1 to 5.
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| CN115272950A (en) * | 2022-06-20 | 2022-11-01 | 浙江齐安信息科技有限公司 | A method and system for locating personnel based on target MAC address and surveillance video |
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