WO2021043074A1

WO2021043074A1 - Urban pet motion trajectory monitoring method based on image recognition, and related devices

Info

Publication number: WO2021043074A1
Application number: PCT/CN2020/111880
Authority: WO
Inventors: 金晨
Original assignee: 平安科技（深圳）有限公司
Priority date: 2019-09-03
Filing date: 2020-08-27
Publication date: 2021-03-11
Also published as: CN110751675B; CN110751675A

Abstract

The present application relates to the technical field of artificial intelligence. Provided are an urban pet motion trajectory monitoring method and apparatus based on image recognition, a terminal, and a storage medium. The method comprises: initializing a category probability of a pet category; acquiring pet images and a geographical position, device identification number and collection time of an image collection device; recognizing identification information of a pet, and storing same and the pet images in an associated manner; when geographical positions, device identification numbers and collection times are all the same, using a first correction model to correct the category probability; when the geographical positions and the device identification numbers are the same, but the collection times are different, using a second correction model to correct the category probability; when the collection times are the same, but the geographical positions and the device identification numbers are all different, using a third correction model to correct the category probability; and determining a motion trajectory of the pet on the basis of the corrected category probability. The present application can be applied to the field of smart city, and the motion trajectory of the pet in a city can be monitored on the basis of probability.

Description

Urban pet activity track monitoring method and related equipment based on image recognition

This application claims the priority of the Chinese patent application filed with the Chinese Patent Office on September 3, 2019, the application number is 201910829499.X, and the invention title is "Image recognition-based urban pet activity track monitoring method and related equipment", all of which The content is incorporated in this application by reference.

Technical field

This application relates to the field of artificial intelligence technology, and in particular to a method, device, terminal and storage medium for monitoring urban pet activity tracks based on image recognition.

Background technique

In recent years, with the improvement of people’s living standards, the number of pets kept by urban residents has increased. When humans enjoy the material and spiritual satisfaction brought by pets, they should also be kind to pets, promote harmonious coexistence between humans and pets, and build wisdom. The idea of the city.

In the prior art, tracking the activity trajectory of urban pets mainly uses video surveillance to analyze and identify moving targets, so as to record the moving process of the targets, which is convenient for tracking and analysis. However, the inventor realized that most of the pets are cats and dogs. These pets are relatively active and run faster. However, using video surveillance to analyze the data collected by multiple cameras, the results obtained are static images without temporality. Continuity. Each camera saves the video data monitored so far. As the monitored target moves, the activity track appears within the monitoring range of different cameras, which causes the data of the monitored target's activity track to be recorded in different camera files, and the target is tracked and The analysis brought great difficulties and affected the tracking and analysis of the pet's trajectory in the later stage.

Therefore, it is necessary to provide a new solution to monitor the activity area of urban pets.

Summary of the invention

In view of the above content, it is necessary to propose a method, device, terminal and storage medium for monitoring the trajectory of urban pets based on image recognition, which can monitor the trajectory of pets in the city based on probability.

The first aspect of the present application provides a method for monitoring urban pet activity tracks based on image recognition, the method including:

Initialize the category probability of each pet category;

Acquiring pet images and collection information collected by an image collection device, where the collection information includes the geographic location, device identification number, and collection time of the image collection device;

Identifying identification information of the pet in the pet image and storing the identification information in association with the pet image and the collected information;

Determine whether the collected information of any two pet images is the same;

When the geographic location, device identification number, and collection time are all the same, use the first correction model to correct the category probability to obtain the first category probability;

When the geographic location and the device identification number are the same, but the collection time is different, the second correction model is used to correct the category probability to obtain the second category probability;

When the collection time is the same, but the geographic location and the device identification number are not the same, the third correction model is used to correct the category probability to obtain the third category probability;

The activity trajectory of the pet is determined based on the corrected category probability.

A second aspect of the present application provides a device for monitoring urban pet activity tracks based on image recognition, the device comprising:

The probability initial module is used to initialize the category probability of each pet category;

An information acquisition module for acquiring pet images and collection information collected by an image collection device, the collection information including the geographic location, device identification number, and collection time of the image collection device;

An identification recognition module for identifying identification information of the pet in the pet image and storing the identification information in association with the pet image and the collected information;

The information judgment module is used to judge whether the collected information of any two pet images is the same;

The first correction module is configured to use the first correction model to correct the category probability to obtain the first category probability when the geographic location, device identification number, and collection time are all the same;

The second correction module is configured to use a second correction model to correct the category probability to obtain the second category probability when the geographic location and the device identification number are the same but the collection time is different;

The third correction module is configured to use the third correction model to correct the category probability to obtain the third category probability when the collection time is the same but the geographic location and the device identification number are different;

The trajectory determination module is used to determine the activity trajectory of the pet based on the corrected category probability.

A third aspect of the present application provides a terminal, the terminal includes a processor, and the processor is configured to implement the following steps when executing computer-readable instructions stored in a memory:

Initialize the category probability of each pet category;

Determine whether the collected information of any two pet images is the same;

A fourth aspect of the present application provides a computer-readable storage medium having computer-readable instructions stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor, the following steps are implemented:

Initialize the category probability of each pet category;

Determine whether the collected information of any two pet images is the same;

In summary, the image recognition-based urban pet activity track monitoring method, device, terminal, and storage medium described in this application can be applied to the management of smart pets, thereby promoting the development of smart cities. This application corrects the initialized category probabilities through multiple parameter information in the collected information, so that the pets in the pet images are getting closer and closer to the real pet categories, especially for the category probabilities of pets appearing in different image collection devices. Finally, the pet image, identification information and collection information are correlated based on the corrected category probability, and the pet’s activity track is determined based on the correlated information. The entire process does not need to identify the specific category of the pet, which can avoid the problem of inaccurate feature vector extraction of pet images by traditional algorithms.

Description of the drawings

In order to more clearly describe the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only These are the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on the provided drawings without creative work.

FIG. 1 is a flowchart of a method for monitoring urban pet activity tracks based on image recognition provided in Embodiment 1 of the present application.

Fig. 2 is a structural diagram of a device for monitoring urban pet activity tracks based on image recognition provided in the second embodiment of the present application.

FIG. 3 is a schematic structural diagram of a terminal provided in Embodiment 3 of the present application.

The following specific embodiments will further illustrate this application in conjunction with the above-mentioned drawings.

detailed description

In order to be able to understand the above objectives, features and advantages of the application more clearly, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the application and the features in the embodiments can be combined with each other if there is no conflict.

In the following description, many specific details are set forth in order to fully understand the present application, and the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of this application.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of this application. The terminology used in the specification of the application herein is only for the purpose of describing specific embodiments, and is not intended to limit the application.

Example one

In this embodiment, for a terminal that needs to perform urban pet activity track monitoring based on image recognition, the function of urban pet activity track monitoring based on image recognition provided by the method of this application can be directly integrated on the terminal, or developed by software The tool kit (Software Development Kit, SKD) runs in the terminal.

As shown in FIG. 1, the method for monitoring urban pet activity tracks based on image recognition specifically includes the following steps. According to different needs, the order of the steps in the flowchart can be changed, and some of the steps can be omitted.

S11, initialize the category probability of each pet category.

In this embodiment, the category probability refers to the probability that a certain pet belongs to a certain category. The category probability is initialized first, and the category probabilities of all pet categories are assigned the same initial value, assuming that a certain pet belongs to each category The initial category probability is the same.

It is possible to enumerate the types of pets that may appear in the city, and then initialize the category probabilities based on the enumerated categories, so that the probability of each category is the same and the total is 1.

Exemplarily, suppose that the pets that may appear in the city are: Golden Retriever, Samoyed, Husky, German Shepherd, Horse Dog, etc., then 5 categories can be set correspondingly, and the category probability of each category is 1/5. The category probability can be initialized or modified according to actual needs.

After initializing the category probability of each category, the category probability and the identification information of each category are stored.

S12: Acquire pet images and collection information collected by an image collection device, where the collection information includes the geographic location, device identification number, and collection time of the image collection device.

In this embodiment, a plurality of high-definition digital image acquisition devices may be preset to collect images of pets according to relevant policy regulations or actual scene requirements.

The presetting a plurality of image acquisition devices includes presetting the positions of the plurality of image acquisition devices and the height of the image acquisition devices. Exemplarily, assuming that pets are prohibited from entering the park, the image capture device can be installed at the entrance and exit of the park or in an open area. When the installation position of the image acquisition device is determined, the installation height of the image acquisition device is determined, so that the pet image collected by the image acquisition device is unobstructed, which is convenient for improving the recognition accuracy of the pet image.

In this embodiment, it is also possible to set a unique device identification number corresponding to each high-definition digital image acquisition device, which is used to indicate the identity of the high-definition digital image acquisition device.

The collection information refers to the information when the image collection device collects the pet image, and may include: the geographic location of the image collection device, the device identification number of the image collection device, and the time when the pet image was collected (hereinafter referred to as Acquisition time). The geographic location may be represented by latitude and longitude coordinates, the device identification number may be represented by C+digits, and the collection time may be represented by year-month-day-hour-minute-second.

S13: Identify identification information of the pet in the pet image and store the identification information in association with the pet image and the collected information.

There are different identification information, that is, identification information and pets have a one-to-one correspondence, for example, golden retriever corresponds to identification information a1, Samoyed corresponds to identification information a2, and Husky corresponds to identification information a3.

After the identification information corresponding to the pet in the pet image is identified, it can be associated with the pet image and the geographic location of the image acquisition device, the device identification number of the image acquisition device, and the time when the pet image was collected and stored in a preset In the database.

Exemplarily, suppose that at a certain time T (time), an image capture device C (camera) located at a certain geographic location L (location) has captured a husky, and the comparison of the husky by the above steps S11-S13 If the identification information is a3, a record (a3, T, L, C) can be formed for associative storage. It is convenient to subsequently obtain other multiple parameter information according to any one parameter association. For example, multiple parameters such as pet images with the same device identification number, identification information, geographic location of the image collection device, and time when the pet image was collected can be obtained in association according to the parameter of the device identification number.

In an optional embodiment, the identifying identification information of the pet in the pet image and storing the identification information in association with the pet image and the collected information includes:

Input the pet image into a pre-trained pet identification recognition model;

Acquiring the recognition result of the pet identification recognition model;

The identification information of the pet is determined according to the recognition result.

In this embodiment, the pet identification recognition model is pre-trained, and the training process may include: acquiring a plurality of pet images in advance; dividing the plurality of pet images and identification information into a training set of a first proportion and a second proportion The test set, wherein the first ratio is much larger than the second ratio; input the training set into a preset deep neural network for supervised learning and training, and obtain a pet identification recognition model; input the test set into the The pet identification recognition model is tested to obtain a test pass rate; when the test pass rate is greater than or equal to the preset pass rate threshold, the training of the pet identification recognition model is ended, and when the test pass rate is less than the preset pass rate Rate threshold, then re-divide the training set and test set, learn and train the pet identification recognition model based on the new training set, and test the pass rate of the newly trained pet identification identification model based on the new test set. Since the pet identification model is not the focus of this application, the specific process of training the pet identification model will not be elaborated here.

In an optional embodiment, the input of the pet image into a pre-trained pet identification recognition model includes:

The target area in the pet image is detected.

Crop the target area in the pet image;

The cropped target area is used as an input image and input into a pre-trained gesture recognition model.

In this embodiment, the YOLO target detection algorithm can be used to select the area of the pet in the pet image with a detection frame. The area selected by the detection frame is the target area, because the number of pixels in the target area is much smaller than that of the entire pet image. The number of pixels, and the target area almost only contains the target object of pets, and no other non-target objects. Therefore, cropping out the target area as the input image of the pet identification model will not only help to improve the pet identification model's recognition of pet identification. The efficiency of information, and the absence of interference from non-target objects in the target area, can also improve the accuracy of the pet identification model for identifying pet identification information.

S14: Determine whether the collection information of any two pet images is the same.

In this embodiment, any two pet images can be obtained from a preset database, and based on the identification information and collection information associated with the two pet images, it is determined whether the pets in the two pet images belong to the same category, and based on the identification information And collect information to modify the initialized category probability. The probability of a certain pet belonging to a certain category is large, and the probability of belonging to other categories is small. Later, the activity trajectory and activity area of pets of different categories can be analyzed based on the corrected category probability.

S15: When the geographic location, device identification number, and collection time are all the same, use the first correction model to correct the category probability to obtain the first category probability.

In this embodiment, the collection information corresponding to any two pet images acquired is the same, that is, the geographic location, device identification number, and acquisition time are exactly the same, indicating that the two pet images were acquired by the same image acquisition device at the same time. .

Suppose that the image acquisition device is represented by c, the geographic location is represented by l, the population is represented by p, and the pet identification is represented by a. A belongs to the population p and is denoted as a ∈ p, and the probability of a ∈ p is ρ

A certain camera c collects two pets i1 and i2 at a certain time t, then there are

with

And the corresponding category probability is

with

In an optional embodiment, the using the first correction model to correct the category probability to obtain the first category probability includes:

Use the following formula to correct the category probability;

Use the following formula to normalize the corrected category probability to obtain the first category probability;

Among them, γ is the correction factor coefficient,

with

Respectively, the initial category probability of two pets collected by the same image acquisition device at the same time,

Is the first category probability.

The foregoing embodiment is based on the category probability correction algorithm of a single image acquisition device at the same time, and pets appearing in a scene at the same time add a weight γ to the same population factor.

S16: When the geographic location and the device identification number are the same, but the collection time is different, the second correction model is used to correct the category probability to obtain the second category probability.

In this embodiment, the geographic locations and device identification numbers corresponding to any two acquired pet images are the same, and the acquisition time is different, indicating that the two pet images were acquired by the same image acquisition device at different times.

A certain camera c collects two different pets i1 and i2 at different times t1 and t2, then there is

with

In an optional embodiment, the second correction model is used to correct the category probability to obtain the second category probability as follows:

Use the following formula to correct the category probability;

Use the following formula to normalize the corrected category probability to obtain the second category probability;

among them,

Is the penalty factor, γ is the correction factor coefficient, t is the time,

with

Are the initial category probabilities of two pets collected by the same image acquisition device at different times,

Is the second category probability.

The above embodiment is based on the category probability correction algorithm of a single image acquisition device at different times. Pets that appear in the same scene within a short period of time are given a penalty factor β _t _{according to the time interval, and a weight value β t} * is added to the same population factor. γ, that is, add a penalty factor β _t to the correction factor coefficient γ, and β _t is related to the interval of time t.

S17: When the collection time is the same but the geographic location and the device identification number are different, the third correction model is used to correct the category probability to obtain the third category probability.

In this embodiment, the geographic locations and device identification numbers corresponding to any two pet images acquired are not the same, but when the acquisition time is the same, it indicates that the two pet images were acquired by two different image acquisition devices at the same time. of.

Cameras c1 and c2 collect pets i1, i2 and i3, i4 at the same time t, respectively

So there is

with

In an optional embodiment, the third category probability is corrected by using the third correction model to obtain the third category probability as follows:

Use the following formula to correct the category probability;

Use the following formula to normalize the corrected category probability to obtain the third category probability;

among them,

Is the penalty factor, γ is the correction factor coefficient,

l is the distance,

with

Are the initial category probabilities of two pets collected by different image collection devices at the same time,

with

Is the third category probability.

The above embodiment is based on the category probability correction algorithm of multiple image acquisition devices at the same time, in which i1 and i3 are matched to the same pet through a matching algorithm (but two cameras i1 and i3 that are far apart cannot be the same pet), Therefore, the correction factor β _l at this time is related to the distance l.

S18: Determine an activity track of the pet based on the corrected category probability.

In this embodiment, after the category probabilities of any two pets are corrected based on the collected information, the corrected category probabilities, pet images, collected information, and identification information can be stored in association, and based on the associated stored information, the category probabilities of the same category can be obtained. The activity trajectory of the pet, and the activity area of the pet is determined according to the activity trajectory.

In an optional embodiment, the determining the pet's activity track based on the corrected category probability includes:

Obtain all the corrected category probabilities corresponding to each pet image;

Selecting the largest category probability from all the corrected category probabilities as the target category probability of the pet image;

Obtain collection information corresponding to pet images with the same target category probability;

The activity track of the pet is determined according to the collected information.

Exemplarily, if the corrected category probabilities of a1 corresponding to Golden Retriever, Samoyed, Husky, German Shepherd, and Horse Dog at t1 are 0.9, 0.1, 0, 0, 0, respectively, and a1 at t2 corresponds to Golden Retriever, Samoyed, Husky, The corrected category probabilities of German Shepherd and Horse Dog are 0.9, 0, 0.1, 0, 0, and the corrected category probabilities of a1 corresponding to Golden Retriever, Samoyed, Husky, German Shepherd and Horse Dog at t2 are 0.8 and 0.1 respectively. , 0.1, 0, 0, the category probability 0.9 is used as the target category probability of a1, indicating that a1 belongs to the golden retriever. At this time, the collection information of all pet images corresponding to a1 is extracted, and then the activity trajectory of a1 is determined according to the extracted collection information. Specifically, according to the location and machine number of the image acquisition device in the acquisition information, and the corresponding acquisition time, it is determined when and where the puppy appears.

You can also display the pet's trajectory in the form of a map.

It should be noted that the above-mentioned urban pet population monitoring method based on image recognition can be used not only to find lost pets, but also to rescue stray pets and law enforcement basis for prohibiting pets from entering specific areas.

In summary, the image recognition-based urban pet activity track monitoring method described in this application can be applied to the management of smart pets, thereby promoting the development of smart cities. This application initializes the category probability of each pet category, acquires pet images and collection information sent by an image collection device, the collection information includes the geographic location of the image collection device, the device identification number, and the collection time, and identifies the pet image The identification information of the pet and the associated storage of the identification information with the pet image and the collection information to determine whether the collection information of any two pet images are the same, when the geographic location, the device identification number and the collection information in the collection information When the collection time is the same, the first correction model is used to update the category probability to obtain the first category probability. When the geographic location and device identification number in the collection information are the same, but the collection time is different, the second correction model is used The category probability is updated to obtain the second category probability. When the collection time in the collection information is the same but the geographic location and the device identification number are different, the third correction model is used to update the category probability to obtain the second category probability. The three-category probabilities are used to determine the pet's activity trajectory based on the corrected category probabilities. This application corrects the initialized category probabilities through multiple parameter information in the collected information, so that the pets in the pet images are getting closer and closer to the real pet categories, especially for the category probabilities of pets appearing in different image collection devices. Finally, the pet image, identification information and collection information are correlated based on the corrected category probability, and the pet’s activity track is determined based on the correlated information. The entire process does not need to identify the specific category of the pet, which can avoid the problem of inaccurate extraction of the feature vector of the pet image by the traditional algorithm.

Example two

In some embodiments, the device 20 for monitoring urban pet activity tracks based on image recognition may include multiple functional modules composed of computer-readable instruction segments. The computer-readable instructions of each program segment in the urban pet activity track monitoring device 20 based on image recognition can be stored in the memory of the terminal and executed by the at least one processor to execute based on image recognition (see Figure 1 describes) the monitoring of urban pet activity tracks.

In this embodiment, the image recognition-based urban pet activity track monitoring device 20 can be divided into multiple functional modules according to the functions it performs. The functional modules may include: a probability initial module 201, an information acquisition module 202, an identification recognition module 203, an information judgment module 204, a first correction module 205, a second correction module 206, a third correction module 207, and a trajectory determination module 208. The module referred to in this application refers to a series of computer-readable instruction segments that can be executed by at least one processor and can complete fixed functions, and are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

The probability initial module 201 is used to initialize the category probability of each pet category.

The information acquisition module 202 is configured to acquire pet images and collection information collected by an image collection device, and the collection information includes the geographic location, device identification number, and collection time of the image collection device.

The identification recognition module 203 is configured to identify the identification information of the pet in the pet image and store the identification information in association with the pet image and the collected information.

Exemplarily, suppose that at a certain time T (time), an image capture device C (camera) located at a certain geographic location L (location) has captured a husky, and the above-mentioned modules 201-203 compare the husky's If the identification information is a3, a record (a3, T, L, C) can be formed for associative storage. It is convenient to subsequently obtain other multiple parameter information according to any one parameter association. For example, multiple parameters such as pet images with the same device identification number, identification information, geographic location of the image collection device, and time when the pet image was collected can be obtained in association according to the parameter of the device identification number.

In an optional embodiment, the identification recognition module 203 identifying the identification information of the pet in the pet image and storing the identification information in association with the pet image and the collected information includes:

Input the pet image into a pre-trained pet identification recognition model;

Acquiring the recognition result of the pet identification recognition model;

The target area in the pet image is detected.

Crop the target area in the pet image;

The information judging module 204 is used to judge whether the collected information of any two pet images is the same.

The first correction module 205 is configured to use the first correction model to correct the category probability to obtain the first category probability when the geographic location, device identification number, and collection time are all the same.

with

And the corresponding category probability is

with

In an optional embodiment, that the first correction module 205 uses the first correction model to correct the category probability to obtain the first category probability includes:

Use the following formula to correct the category probability;

Among them, γ is the correction factor coefficient,

with

Is the first category probability.

The second correction module 206 is configured to use a second correction model to correct the category probability to obtain the second category probability when the geographic location and the device identification number are the same but the collection time is different.

with

In an optional embodiment, the second correction module 206 uses a second correction model to correct the category probability to obtain the second category probability as follows:

Use the following formula to correct the category probability;

among them,

Is the penalty factor, γ is the correction factor coefficient, t is the time,

with

Is the second category probability.

The third correction module 207 is configured to use the third correction model to correct the category probability to obtain the third category probability when the collection time is the same but the geographic location and the device identification number are different.

So there is

with

In an optional embodiment, the third correction module 207 uses a third correction model to correct the category probability to obtain the third category probability as follows:

Use the following formula to correct the category probability;

among them,

Is the penalty factor, γ is the correction factor coefficient,

l is the distance,

with

Respectively, the initial category probabilities of two pets collected by different image collection devices at the same time,

Is the third category probability.

The trajectory determination module 208 is configured to determine the activity trajectory of the pet based on the corrected category probability.

In an optional embodiment, the trajectory determination module 208 determining the activity trajectory of the pet based on the corrected category probability includes:

You can also display the pet's trajectory in the form of a map.

It should be noted that the above-mentioned urban pet population monitoring method based on image recognition can be applied not only to finding lost pets, but also to rescue stray pets and law enforcement basis for prohibiting pets from entering specific areas.

In summary, the urban pet activity track monitoring device based on image recognition described in this application can be applied to the management of smart pets, thereby promoting the development of smart cities. This application initializes the category probability of each pet category, acquires pet images and collection information sent by an image collection device, the collection information includes the geographic location of the image collection device, the device identification number, and the collection time, and identifies the pet image The identification information of the pet and the associated storage of the identification information with the pet image and the collection information to determine whether the collection information of any two pet images are the same, when the geographic location, the device identification number and the collection information in the collection information When the collection time is the same, the first correction model is used to update the category probability to obtain the first category probability. When the geographic location and device identification number in the collection information are the same, but the collection time is different, the second correction model is used The category probability is updated to obtain the second category probability. When the collection time in the collection information is the same but the geographic location and the device identification number are different, the third correction model is used to update the category probability to obtain the second category probability. The three-category probabilities are used to determine the pet's activity trajectory based on the corrected category probabilities. This application corrects the initialized category probabilities through multiple parameter information in the collected information, so that the pets in the pet images are getting closer and closer to the real pet categories, especially for the category probabilities of pets appearing in different image collection devices. Finally, the pet image, identification information and collection information are correlated based on the corrected category probability, and the pet’s activity track is determined based on the correlated information. The entire process does not need to identify the specific category of the pet, which can avoid the problem of inaccurate extraction of the feature vector of the pet image by the traditional algorithm.

Example three

Refer to FIG. 3, which is a schematic structural diagram of a terminal provided in Embodiment 3 of this application. In a preferred embodiment of the present application, the terminal 3 includes a memory 31, at least one processor 32, at least one communication bus 33, and a transceiver 34.

Those skilled in the art should understand that the structure of the terminal shown in FIG. 3 does not constitute a limitation of the embodiment of the present application. It may be a bus-type structure or a star structure. The terminal 3 may also include more More or less other hardware or software, or different component arrangements.

In some embodiments, the terminal 3 is a terminal that can automatically perform numerical calculation and/or information processing in accordance with pre-set or stored instructions. Its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit, and Programming gate arrays, digital processors and embedded devices, etc. The terminal 3 may also include client equipment. The client equipment includes, but is not limited to, any electronic product that can interact with the client through a keyboard, a mouse, a remote control, a touch panel, or a voice control device, for example, a personal computer. Computers, tablets, smart phones, digital cameras, etc.

It should be noted that the terminal 3 is only an example. If other existing or future electronic products can be adapted to this application, they should also be included in the protection scope of this application and included here by reference.

In some embodiments, the memory 31 is used to store computer-readable instructions and various data, such as a device installed in the terminal 3, and realize high-speed and automatic completion of programs or data during the operation of the terminal 3 Access. The memory 31 includes volatile and non-volatile memory, such as random access memory (Random Access Memory, RAM), read-only memory (Read-Only Memory, ROM), and programmable read-only memory (Programmable Read-Only). Memory, PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electronic Erasable Programmable Read-Only Memory, OTPROM Read memory (Electrically-Erasable Programmable Read-Only Memory, EEPROM), CD-ROM (Compact Disc Read-Only Memory, CD-ROM) or other optical disk storage, magnetic disk storage, tape storage, or capable of carrying or storing data Computer-readable storage medium. The computer-readable storage medium may be non-volatile or volatile.

In some embodiments, the at least one processor 32 may be composed of integrated circuits, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits with the same function or different functions, including one Or a combination of multiple central processing units (CPU), microprocessors, digital processing chips, graphics processors, and various control chips. The at least one processor 32 is the control core (Control Unit) of the terminal 3. Various interfaces and lines are used to connect the various components of the entire terminal 3, and by running or executing programs or modules stored in the memory 31, And call the data stored in the memory 31 to execute various functions of the terminal 3 and process data.

In some embodiments, the at least one communication bus 33 is configured to implement connection and communication between the memory 31 and the at least one processor 32 and the like.

Although not shown, the terminal 3 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 32 through a power management device, so as to realize management through the power management device. Functions such as charging, discharging, and power management. The power supply may also include any components such as one or more DC or AC power supplies, recharging devices, power failure detection circuits, power converters or inverters, and power status indicators. The terminal 3 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

It should be understood that the embodiments are only for illustrative purposes, and are not limited by this structure in the scope of the patent application.

The above-mentioned integrated unit implemented in the form of a software function module may be stored in a computer readable storage medium. The above-mentioned software function module is stored in a storage medium and includes several instructions to make a computer device (which may be a personal computer, a terminal, or a network device, etc.) or a processor execute the method described in each embodiment of the present application. section.

In a further embodiment, with reference to FIG. 2, the at least one processor 32 can execute the operating device of the terminal 3 and various installed applications, computer-readable instructions, etc., such as the above-mentioned modules.

The memory 31 stores computer-readable instructions, and the at least one processor 32 can call the computer-readable instructions stored in the memory 31 to perform related functions. For example, the various modules described in FIG. 2 are computer-readable instructions stored in the memory 31 and executed by the at least one processor 32, so as to realize the functions of the various modules.

In an embodiment of the present application, the memory 31 stores multiple instructions, and the multiple instructions are executed by the at least one processor 32 to implement all or part of the steps in the method described in the present application.

Specifically, for the specific implementation method of the at least one processor 32 on the foregoing instructions, reference may be made to the description of the relevant steps in the embodiment corresponding to FIG.

In the several embodiments provided in this application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

In addition, the functional modules in the various embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware, or may be implemented in the form of hardware plus software functional modules.

For those skilled in the art, it is obvious that the present application is not limited to the details of the foregoing exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of this application is defined by the appended claims rather than the above description, and therefore it is intended to fall into the claims. All changes in the meaning and scope of the equivalent elements of are included in this application. Any reference signs in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "including" does not exclude other elements or the singular does not exclude the plural. Multiple units or devices stated in the device claims can also be implemented by one unit or device through software or hardware. Words such as first and second are used to denote names, but do not denote any specific order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application and not to limit them. Although the application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the application can be Make modifications or equivalent replacements without departing from the spirit and scope of the technical solution of the present application.

Claims

An image recognition-based urban pet activity track monitoring method, wherein the method includes:

Initialize the category probability of each pet category;

Acquiring pet images and collection information collected by an image collection device, where the collection information includes the geographic location, device identification number, and collection time of the image collection device;

Identifying identification information of the pet in the pet image and storing the identification information in association with the pet image and the collected information;

Determine whether the collected information of any two pet images is the same;

When the geographic location, device identification number, and collection time are all the same, use the first correction model to correct the category probability to obtain the first category probability;

When the geographic location and the device identification number are the same, but the collection time is different, the second correction model is used to correct the category probability to obtain the second category probability;

When the collection time is the same, but the geographic location and the device identification number are not the same, the third correction model is used to correct the category probability to obtain the third category probability;

The activity trajectory of the pet is determined based on the corrected category probability.
The method for monitoring the activity trajectory of urban pets based on image recognition according to claim 1, wherein said adopting a first correction model to correct said category probability to obtain a first category probability comprises:

Use the following formula to correct the category probability;

Use the following formula to normalize the corrected category probability to obtain the first category probability;

Among them, γ is the correction factor coefficient,
with
Respectively, the initial category probability of two pets collected by the same image acquisition device at the same time,
Is the first category probability.
The method for monitoring the activity trajectory of urban pets based on image recognition according to claim 1, wherein said adopting a second correction model to correct said category probability to obtain a second category probability comprises:

Use the following formula to correct the category probability;

Use the following formula to normalize the corrected category probability to obtain the second category probability;

among them,
Is the penalty factor, γ is the correction factor coefficient, t is time,
with
Are the initial category probabilities of two pets collected by the same image acquisition device at different times,
Is the second category probability.
The method for monitoring the activity trajectory of urban pets based on image recognition according to claim 1, wherein said adopting a third correction model to correct said category probability to obtain a third category probability comprises:

Use the following formula to correct the category probability;

Use the following formula to normalize the corrected category probability to obtain the third category probability;

among them,
Is the penalty factor, γ is the correction factor coefficient,
l is the distance,
with
Are the initial category probabilities of two pets collected by different image collection devices at the same time,
with
Are the initial category probabilities of two pets collected by different image collection devices at the same time,
Is the third category probability.
The method for monitoring the activity track of urban pets based on image recognition according to any one of claims 1 to 4, wherein the identifying information of the pet in the pet image and combining the identification information with the pet image And the associated storage of the collected information includes:

Input the pet image into a pre-trained pet identification recognition model;

Acquiring the recognition result of the pet identification recognition model;

The identification information of the pet is determined according to the recognition result.
The method for monitoring urban pet activity tracks based on image recognition according to any one of claims 1 to 4, wherein said inputting said pet image into a pre-trained pet identification recognition model comprises:

Detecting the target area in the pet image;

Crop the target area in the pet image;

The cropped target area is used as an input image and input into a pre-trained gesture recognition model.
The method for monitoring the activity trajectory of urban pets based on image recognition according to any one of claims 1 to 4, wherein the determining the activity trajectory of the pet based on the corrected category probability comprises:

Obtain all the corrected category probabilities corresponding to each pet image;

Selecting the largest category probability from all the corrected category probabilities as the target category probability of the pet image;

Obtain collection information corresponding to pet images with the same target category probability;

The activity track of the pet is determined according to the collected information.
An image recognition-based urban pet activity track monitoring device, wherein the device includes:

The probability initial module is used to initialize the category probability of each pet category;

An information acquisition module for acquiring pet images and collection information collected by an image collection device, the collection information including the geographic location, device identification number, and collection time of the image collection device;

An identification recognition module for identifying identification information of the pet in the pet image and storing the identification information in association with the pet image and the collected information;

The information judgment module is used to judge whether the collected information of any two pet images is the same;

The first correction module is configured to use the first correction model to correct the category probability to obtain the first category probability when the geographic location, device identification number, and collection time are all the same;

The second correction module is configured to use a second correction model to correct the category probability to obtain the second category probability when the geographic location and the device identification number are the same but the collection time is different;

The third correction module is configured to use the third correction model to correct the category probability to obtain the third category probability when the collection time is the same but the geographic location and the device identification number are different;

The trajectory determination module is used to determine the activity trajectory of the pet based on the corrected category probability.
A terminal, wherein the terminal includes a processor configured to execute computer-readable instructions stored in a memory to implement the following steps:

Initialize the category probability of each pet category;

Acquiring pet images and collection information collected by an image collection device, where the collection information includes the geographic location, device identification number, and collection time of the image collection device;

Identifying identification information of the pet in the pet image and storing the identification information in association with the pet image and the collected information;

Determine whether the collected information of any two pet images is the same;

When the geographic location, device identification number, and collection time are all the same, use the first correction model to correct the category probability to obtain the first category probability;

When the geographic location and the device identification number are the same, but the collection time is different, the second correction model is used to correct the category probability to obtain the second category probability;

When the collection time is the same, but the geographic location and the device identification number are not the same, the third correction model is used to correct the category probability to obtain the third category probability;

The activity trajectory of the pet is determined based on the corrected category probability.
The terminal according to claim 9, wherein when the processor executes the computer-readable instructions to implement the first correction model to correct the category probability to obtain the first category probability, the specific steps include:

Use the following formula to correct the category probability;

Use the following formula to normalize the corrected category probability to obtain the first category probability;

Among them, γ is the correction factor coefficient,
with
Respectively, the initial category probability of two pets collected by the same image acquisition device at the same time,
Is the first category probability.
The terminal according to claim 9, wherein, when the processor executes the computer-readable instruction to implement the second correction model to correct the category probability to obtain the second category probability, it specifically includes:

Use the following formula to correct the category probability;

Use the following formula to normalize the corrected category probability to obtain the second category probability;

among them,
Is the penalty factor, γ is the correction factor coefficient, t is the time,
with
Are the initial category probabilities of two pets collected by the same image acquisition device at different times,
Is the second category probability.
The terminal according to claim 9, wherein, when the processor executes the computer-readable instructions to implement a third correction model to correct the category probability to obtain the third category probability, the specific steps specifically include:

Use the following formula to correct the category probability;

Use the following formula to normalize the corrected category probability to obtain the third category probability;

among them,
Is the penalty factor, γ is the correction factor coefficient,
l is the distance,
with
Respectively, the initial category probabilities of two pets collected by different image collection devices at the same time,
with
Respectively, the initial category probabilities of two pets collected by different image collection devices at the same time,
Is the third category probability.
The terminal according to any one of claims 9 to 12, wherein the processor executes the computer-readable instructions to realize the identification information of the pet in the pet image and the identification information is connected with the identification information of the pet. When the pet image and the collected information are stored in association, it specifically includes:

Input the pet image into a pre-trained pet identification recognition model;

Acquiring the recognition result of the pet identification recognition model;

The identification information of the pet is determined according to the recognition result.
The terminal according to any one of claims 9 to 12, wherein when the processor executes the computer-readable instructions to input the pet image into a pre-trained pet identification recognition model, it specifically includes:

Detecting the target area in the pet image;

Crop the target area in the pet image;

The cropped target area is used as an input image and input into a pre-trained gesture recognition model.
The terminal according to any one of claims 9 to 12, wherein when the processor executes the computer-readable instructions to determine the pet's activity trajectory based on the corrected category probability, it specifically includes:

Obtain all the corrected category probabilities corresponding to each pet image;

Selecting the largest category probability from all the corrected category probabilities as the target category probability of the pet image;

Obtain collection information corresponding to pet images with the same target category probability;

The activity track of the pet is determined according to the collected information.
A computer-readable storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions implement the following steps when executed by a processor:

Initialize the category probability of each pet category;

Acquiring pet images and collection information collected by an image collection device, where the collection information includes the geographic location, device identification number, and collection time of the image collection device;

Identifying identification information of the pet in the pet image and storing the identification information in association with the pet image and the collected information;

Determine whether the collected information of any two pet images is the same;

When the geographic location, device identification number, and collection time are all the same, use the first correction model to correct the category probability to obtain the first category probability;

When the geographic location and the device identification number are the same, but the collection time is different, the second correction model is used to correct the category probability to obtain the second category probability;

When the collection time is the same, but the geographic location and the device identification number are not the same, the third correction model is used to correct the category probability to obtain the third category probability;

The activity trajectory of the pet is determined based on the corrected category probability.
The computer-readable storage medium according to claim 16, wherein the computer-readable instructions are executed by the processor to implement the first correction model to correct the category probability to obtain the first category probability, specifically comprising :

Use the following formula to correct the category probability;

Use the following formula to normalize the corrected category probability to obtain the first category probability;

Among them, γ is the correction factor coefficient,
with
Respectively, the initial category probability of two pets collected by the same image acquisition device at the same time,
Is the first category probability.
The computer-readable storage medium according to claim 16, wherein the computer-readable instructions are executed by the processor to implement the second correction model to correct the category probability to obtain the second category probability, which specifically includes :

Use the following formula to correct the category probability;

Use the following formula to normalize the corrected category probability to obtain the second category probability;

among them,
Is the penalty factor, γ is the correction factor coefficient, t is time,
with
Are the initial category probabilities of two pets collected by the same image acquisition device at different times,
Is the second category probability.
The computer-readable storage medium according to claim 16, wherein when the computer-readable instructions are executed by the processor to implement a third correction model to correct the category probability to obtain the third category probability, it specifically includes :

Use the following formula to correct the category probability;

Use the following formula to normalize the corrected category probability to obtain the third category probability;

among them,
Is the penalty factor, γ is the correction factor coefficient,
l is the distance,
with
Are the initial category probabilities of two pets collected by different image collection devices at the same time,
with
Are the initial category probabilities of two pets collected by different image collection devices at the same time,
Is the third category probability.
The computer-readable storage medium according to any one of claims 16 to 19, wherein the computer-readable instructions are executed by the processor to realize the identification information of the pet in the pet image and the When the identification information is stored in association with the pet image and the collected information, it specifically includes:

Input the pet image into a pre-trained pet identification recognition model;

Acquiring the recognition result of the pet identification recognition model;

The identification information of the pet is determined according to the recognition result.