CN111143703B - Intelligent line recommendation method and related products - Google Patents

Intelligent line recommendation method and related products Download PDF

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CN111143703B
CN111143703B CN201911320923.4A CN201911320923A CN111143703B CN 111143703 B CN111143703 B CN 111143703B CN 201911320923 A CN201911320923 A CN 201911320923A CN 111143703 B CN111143703 B CN 111143703B
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Shanghai Cambricon Information Technology Co Ltd
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Abstract

The embodiment of the invention discloses an intelligent circuit recommending method and related products, which are applied to an intelligent circuit recommending device, wherein the method comprises the following steps: the method comprises the steps that video analysis is conducted on a plurality of obtained shooting videos through a processor to obtain a plurality of video image sets, an artificial intelligent chip sequentially calculates video images in the plurality of video image sets according to a preset artificial intelligent algorithm to obtain a first position where a first object is currently located in a cell and m other positions where m other objects are located, a predictor predicts m reference action tracks of the m other objects according to the m other positions of the m other objects and a plurality of action tracks stored in a memory in advance, and the processor recommends a target action line for the first object according to the m reference action tracks and the first position, so that a recommended advancing route of the first object can be reasonably planned and adjusted according to the other m objects, and activity experience of a user in the cell is improved.

Description

Intelligent line recommendation method and related products
Technical Field
The invention relates to the technical field of video processing, in particular to an intelligent line recommendation method and related products.
Background
At present, urban cells are more and more modern, the activities of cell personnel are more and more diversified, the activity connection in the cells is more and more compact, in order to enable the cell service to be more intelligent, the activity experience of households in the cells is improved, more individual and careful services are required to be provided for the households, and therefore the problems of how to enable the relationship between people to be more compact and the relationship between people and pets to be more harmonious are solved.
Disclosure of Invention
The embodiment of the invention provides an intelligent line recommending method and related products, which improve the activity experience of a user in a cell by pushing a target action line for a first object which is active in the cell.
In a first aspect, an embodiment of the present invention provides an intelligent line recommendation method, applied to an intelligent line recommendation device, where the device includes a processor, an artificial intelligent chip, a predictor, and a memory, the method includes:
the processor analyzes the acquired multiple shot videos to obtain multiple video image sets, wherein the multiple shot videos are shot videos of different areas of a cell in a first preset time period;
the artificial intelligent chip sequentially calculates video images in the plurality of video image sets according to a preset artificial intelligent algorithm to obtain a first position where a first object is currently located in the cell and m other positions where m other objects are located, wherein m is a positive integer, each other object corresponds to one other position, and the first position and the m other positions are located in different areas in the cell;
The predictor predicts m reference action tracks of the m other objects according to m other positions of the m other objects and a plurality of action tracks stored in the memory in advance;
the processor recommends a target action line for the first object according to the m reference action tracks and the first position.
In a second aspect, an embodiment of the present invention provides an intelligent line recommendation apparatus, the apparatus including a processor, an artificial intelligent chip, a predictor, and a memory, wherein,
the processor is used for carrying out video analysis on the acquired multiple shot videos to obtain multiple video image sets, wherein the multiple shot videos are shot videos of different areas of a cell in a first preset time period;
the artificial intelligent chip is used for sequentially calculating the video images in the plurality of video image sets according to a preset artificial intelligent algorithm to obtain a first position where a first object is currently located in the cell and m other positions where m other objects are located, wherein m is a positive integer, each other object corresponds to one other position, and the first position and the m other positions are located in different areas in the cell;
The predictor is used for predicting m reference action tracks of the m other objects according to m other positions of the m other objects and a plurality of action tracks stored in the memory in advance;
the processor is further configured to recommend a target action route for the first object according to the m reference action tracks and the first position.
In a third aspect, an embodiment of the present invention provides an intelligent line recommendation apparatus, including a processor, an artificial intelligent chip, a predictor, a memory, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, the artificial intelligent chip, and the predictor, and the programs include instructions for executing steps in the first aspect of the embodiment of the present invention.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to perform part or all of the steps described in the first aspect of the embodiments of the present invention.
In a fifth aspect, embodiments of the present invention provide a computer program product, wherein the computer program product comprises a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present invention. The computer program product may be a software installation package.
The embodiment of the invention has the following beneficial effects:
according to the intelligent line recommendation method and related products, the obtained multiple shot videos are subjected to video analysis through the processor to obtain multiple video image sets, the artificial intelligent chip sequentially calculates video images in the multiple video image sets according to a preset artificial intelligent algorithm to obtain a first position where a first object is currently located in a cell and m other positions where m other objects are located, the predictor predicts m reference action track processors of the m other objects according to the m other positions of the m other objects and the multiple action tracks stored in the memory in advance, and the m reference action track processors of the m other objects recommend target action tracks for the first object according to the m reference action tracks and the first position, so that the target action tracks can be pushed for the first object according to the m reference action tracks of the other objects in the cell, the processing speed of the video images is improved, the route recommendation can be rapidly and effectively performed, the recommended travelling route of the first object can be reasonably planned and adjusted according to the m other objects, and the activity experience of a user in the cell is improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1A is a system diagram of a pet monitoring system according to an embodiment of the present invention;
fig. 1B is a schematic structural diagram of an intelligent circuit recommendation device according to an embodiment of the present invention;
FIG. 1C is a schematic diagram of an artificial intelligence chip according to an embodiment of the present invention;
fig. 2A is a schematic flow chart of an intelligent circuit recommendation method according to an embodiment of the present invention;
fig. 2B is a schematic illustration of an electronic map according to an embodiment of the present invention;
fig. 3 is a flow chart of another intelligent circuit recommendation method according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of another intelligent circuit recommendation device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an intelligent circuit recommendation device according to an embodiment of the present invention.
Detailed Description
The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The terminal device according to the embodiment of the present application may include various handheld devices, vehicle-mounted devices, wearable devices (smart watches, smart bracelets, wireless headphones, augmented reality/virtual reality devices, smart glasses), computing devices or other processing devices connected to a wireless modem, and various forms of User Equipment (UE), mobile Stations (MS), control platforms, terminal devices (terminal devices), and so on, which have wireless communication functions. For convenience of description, the above-mentioned devices are collectively referred to as terminal devices. The intelligent traffic system is applied to the terminal equipment.
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1A, fig. 1A provides a system schematic diagram of an intelligent line recommendation system for implementing the intelligent line recommendation method. The pet monitoring system can comprise a plurality of cameras, intelligent line recommending devices and terminal equipment of users, wherein the cameras are arranged in all areas of a cell, the intelligent line recommending devices can be connected with the intelligent line recommending devices, the intelligent line recommending devices can be in communication connection with the terminal equipment of the users, videos of all areas of the cell can be shot through the cameras, and therefore the intelligent line recommending devices can acquire a plurality of shooting videos shot by the cameras.
Optionally, the intelligent line recommendation device may include a plurality of cameras disposed in each area of the cell, so that the plurality of cameras may capture video of each area of the cell, and a plurality of captured videos may be obtained.
Further, when the first object is about to go out, the intelligent line recommendation device may recommend a target action line for the first object according to the acquired plurality of shot videos, and further push the target action line to the user.
Referring to fig. 1B, fig. 1B provides an intelligent line recommendation device, which may be implemented to perform intelligent line recommendation for a user, and the intelligent line recommendation device, as shown in fig. 1B, includes: the system comprises a processor 110, an artificial intelligence chip 111, a predictor 112 and a memory 113, wherein the artificial intelligence chip comprises a data converter, a data access circuit, a register circuit and an operation circuit.
The processor 110 may be a general-purpose processor such as a central processing unit (Central Processing Unit, CPU), digital signal processor (Digital Signal Processor, DSP), graphics processor (Graphics Processing Unit, GPU), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Alternatively, the processor 110 may be an artificial intelligence processor.
Referring to fig. 1C, fig. 1C is a schematic structural diagram of an artificial intelligent chip provided in the present application, where the artificial intelligent chip is applied to an intelligent line recommendation device, and the intelligent line recommendation device may further include a processor, a predictor and a memory, in addition to the artificial intelligent chip, where the artificial intelligent chip may be used to perform an image recognition operation, and the artificial intelligent chip may include: the data access circuit, the register circuit, the arithmetic circuit, wherein, the arithmetic circuit can include: two or more of an adder, a multiplier, a comparator, and an activation arithmetic unit. Of course, in practical application, the operation circuit may also include a plurality of addition calculators or a plurality of multiplication calculators, and of course, in practical application, the number of addition calculators, multiplication calculators, comparators and activation calculators included in the operation circuit may not be limited. The register circuit is used for storing the operation instruction, the address of the storage medium of the data block and the calculation topological structure corresponding to the operation instruction.
In a specific implementation, in an intelligent line recommendation scenario, the data converter converts a first video image in the plurality of video image sets into first input data, and transmits the first input data to a data access circuit; the data access circuit sends the first input data to the operation circuit; the data access circuit reads an operation instruction from the register circuit and sends the operation instruction to the operation circuit; the operation circuit performs pixel gradient calculation according to the operation instruction and the first input data to obtain gradient magnitudes and gradient directions of all pixel points in the first video image; and counting the gradient amplitude values and gradient directions of all the pixel points to obtain a feature vector set belonging to the first object in the first video image, and determining a first position where the first object is currently located according to the feature vector set. Through the artificial intelligent chip, video images in the video image sets can be sequentially operated according to a preset artificial intelligent algorithm to obtain m other positions where m other objects are located in the cell, further, the data access circuit can transmit the first position where the first object is currently located to the processor, transmit the m other positions where the m other objects are located to the predictor, the predictor predicts m reference action tracks of the m other objects according to the m other positions of the m other objects and a plurality of action tracks stored in the memory in advance, and finally, the processor recommends a target action line for the first object according to the m reference action tracks and the first position.
Fig. 2A is a schematic flow chart of an intelligent circuit recommendation method according to an embodiment of the present invention. The intelligent line recommending method described in the present embodiment is applied to an intelligent line recommending apparatus as shown in fig. 1B, where the apparatus includes a processor, an artificial intelligent chip, a predictor and a memory, and the method includes the following steps:
201. the method comprises the steps that a plurality of obtained shooting videos are subjected to video analysis by a processor to obtain a plurality of video image sets, wherein the shooting videos are shooting videos of different areas of a cell in a first preset time period.
The first preset time period may be set by the user or default by the system, for example, the first preset time period may be the last 1 minute, or the last 3 minutes, the last 5 minutes, or the like, which is not limited herein.
In the embodiment of the invention, a plurality of cameras can be arranged in each area of a cell, the cameras can be single cameras or multiple cameras, and the single cameras can be infrared cameras, visible light cameras, wide-angle cameras and double cameras. Personnel activities and animal activities of each area in the cell can be shot through the camera, so that the processor can acquire a plurality of shooting videos shot by the camera in a preset time period.
202. The artificial intelligent chip sequentially calculates the video images in the video image sets according to a preset artificial intelligent algorithm to obtain a first position where a first object is currently located in the cell and m other positions where m other objects are located, wherein m is a positive integer, each other object corresponds to one other position, and the first position and the m other positions are located in different areas in the cell.
Wherein the first object may be a person or an animal, and the m other objects may include a person or an animal.
Wherein, the preset artificial intelligence algorithm can comprise any one of the following: a directional gradient histogram (Histogram of Oriented Gradients, HOG) algorithm, a hough transform, a Haar feature cascade classifier algorithm, or a local binary pattern (Local Binary Pattern, LBP) algorithm, etc., without limitation.
In the specific implementation, the artificial intelligent chip can perform object recognition on video images in a plurality of video image sets, determine whether personnel or animals exist in the video images, recognize the personnel or the animals, and further determine the positions of the personnel or the animals.
The following specifically describes an example of the histogram of directional gradients algorithm.
Optionally, the preset artificial intelligence algorithm includes a direction gradient histogram algorithm, the artificial intelligence chip includes a data converter, a data access circuit, a register circuit, and an operation circuit, in the step 202, the step of sequentially performing, by the artificial intelligence chip, operations on video images in the plurality of video image sets according to the preset artificial intelligence algorithm to obtain a first position where a first object in the cell is currently located may include the following steps:
21. the data converter converts a first video image in the plurality of video image sets into first input data and transmits the first input data to a data access circuit;
22. the data access circuit sends the first input data to the operation circuit; the data access circuit reads an operation instruction from the register circuit and sends the operation instruction to the operation circuit;
23. the operation circuit performs pixel gradient calculation according to the operation instruction and the first input data to obtain gradient magnitudes and gradient directions of all pixel points in the first video image; and counting the gradient amplitude values and gradient directions of all the pixel points to obtain a feature vector set belonging to the first object in the first video image, and determining a first position where the first object is currently located according to the feature vector set.
In a specific implementation, the operation circuit can perform pixel gradient calculation according to the first input data to obtain gradient magnitudes and gradient directions of all pixel points in the first video image, and count the gradient magnitudes and gradient directions of all pixel points to obtain a feature vector set belonging to the first object in the first video image, wherein the feature vector set can comprise a plurality of HOG features of the first object, and further, the first position where the first object is currently located can be determined according to the feature vector set, so that the feature extraction speed of the video image can be improved, route recommendation can be performed quickly and effectively, and the activity experience of a user in a cell is further improved.
According to the mode, the artificial intelligent chip can also sequentially operate the video images in the plurality of video image sets according to the preset artificial intelligent algorithm to obtain m other positions where m other objects in the cell are located.
Optionally, before the artificial intelligence chip sequentially performs operations on the video images in the plurality of video image sets according to a preset artificial intelligence algorithm, the method may further include the following steps:
24. the processor determines the similarity among a plurality of video images in a first preset duration in each video image set to obtain at least one similarity set;
25. Taking a plurality of video images with the similarity larger than a preset threshold value in each similarity set in the at least one similarity set as a group of repeated video images to obtain at least one group of repeated video images;
26. performing quality evaluation on each of the at least one set of repeated video images to obtain at least one set of quality evaluation values;
27. selecting a maximum quality evaluation value in each group of quality evaluation values in the at least one group of quality evaluation values to obtain at least one maximum quality evaluation value;
28. and selecting video images corresponding to each maximum quality evaluation value in the at least one maximum quality evaluation value in the plurality of video image sets, and obtaining a plurality of screened video images.
The first preset duration may be set by the user or default by the system, for example, the first preset duration may be 5 seconds or 10 seconds, which is not limited herein.
In order to reduce the number of video images calculated by the artificial intelligence chip on the video images, at least one set of repeated video images in the plurality of video images can be determined first, for example, in one shot video, the same pet is shot to move at the same place in the period from the fifth second to the seventh second, and the similarity between the plurality of video images in the fifth second to the seventh second obtained by analysis is greater than a preset threshold, at this time, the plurality of video images in the period can be determined to be a set of repeated video images, wherein for the video images in any shot video, the similarity between each video image and other video images adjacent to the video image in the preset period can be determined, and if the similarity is greater than the preset threshold, the two video images to be compared can be determined to be repeated video images, so that at least one set of repeated video images can be obtained. Therefore, at least one set of repeated video images in the plurality of videos can be determined, then, each set of repeated video images in the at least one set of repeated video images is subjected to instruction evaluation to obtain a plurality of sets of quality evaluation values, so that a first video image with the best quality in each set of repeated video images in the at least one set of repeated video images can be selected, at least one first video image corresponding to the at least one set of repeated video images is reserved, and at least one video image and other video images without repetition are used as a plurality of target video images.
It can be seen that by partially deleting the repeated video images among the plurality of video images, the number of images to be subjected to subsequent processing can be reduced, the image processing efficiency can be improved, and by retaining the first video image having the highest quality evaluation value among each set of repeated video images, the efficiency of subsequent processing of the first video image can be improved.
203. The predictor predicts m reference action tracks of the m other objects according to m other positions of the m other objects and a plurality of action tracks stored in the memory in advance.
In a specific implementation, a plurality of action tracks may be stored in the memory in advance, where the plurality of action tracks may be a plurality of action tracks that are determined by a plurality of historical videos of a plurality of objects in a cell and are frequently acted by a plurality of objects. For example, if the first preset time period is within a time period range of 6:00-7:00, available for 6 a month more often in the morning each day: 00-7:00 a plurality of objects moving in a cell, determining the action track of each object moving frequently in the plurality of objects, obtaining a plurality of action tracks, and storing the plurality of action tracks in a memory. Therefore, after the predictor obtains m other positions of m other objects in the first preset time period, the predictor predicts the reference action track of each other object in the m other objects to be moved according to the action tracks corresponding to the m other objects and the m other positions of the m other objects stored in advance, and obtains the reference action tracks.
Optionally, in an embodiment of the present application, the method may further include the following steps:
the processor acquires a plurality of historical videos of different areas of the cell within a second preset time period;
performing object recognition according to the plurality of historical videos to obtain a plurality of historical recognition results of a plurality of objects, wherein each historical recognition result comprises a position corresponding to one object, and each historical recognition result corresponds to one shooting time;
determining a plurality of action tracks according to the plurality of historical identification results and a plurality of shooting times corresponding to the plurality of historical identification results one by one, wherein each action track is an action track of an object in the second preset time period;
and storing the action tracks to the memory.
The second preset period may be set by the user or default by the system, for example, the second preset period may be the last week, the last month, the last three months, or the like, which is not limited herein.
In a specific implementation, a plurality of historical videos of different areas of a cell in a second preset time period, for example, a plurality of historical videos of the last month, are acquired, object recognition is performed according to the plurality of historical videos, a plurality of historical recognition results of a plurality of objects are obtained, for example, an animal such as a dog can be recognized, or a person is recognized to frequently appear at a plurality of positions within a certain time period (for example, 6:00-7:00, or 20:00-21:00), then the plurality of positions are connected according to the time sequence of the animal or the person in the time period, so that the action track of the animal or the person in the time period can be obtained, and therefore, a plurality of action tracks of the person or the animal which frequently perform activities in the cell in each time period can be determined according to the plurality of historical videos, and the action tracks of the plurality of objects are stored in a memory.
204. And the processor recommends a target action line for the first object according to the m reference action tracks and the first position.
In this embodiment of the present application, the first object may be a person or an animal, for example, a pet of a user, and the target action line may be recommended for the first object, may be a action line for pushing a walk for the user, or may be an activity line for pushing a pet of the user. In a specific implementation, an action line may be pushed to a user according to a relationship between a first object and m other objects, for example, an acquaintance with the first object in the m other objects or an animal loved by the first object may be pushed to the user, and a target action line that is the same as the acquaintance may be pushed to the user, for example, an animal fear with the first object in the m other objects or a person having a bad relationship with the first object may be pushed to the user, and a target action line avoiding the animal or the person may be pushed to the user. Therefore, the target action line can be pushed to the first object according to m reference action tracks of other objects in the cell, so that the recommended travel route of the first object can be reasonably planned and adjusted according to the other m objects, and further the activity experience feeling of the user in the cell is improved.
Optionally, the first object is a first animal, the m other objects are m animals, or the first object is a first person, the m other objects are m persons, and the processor recommends a target action route for the first object according to the m reference action tracks and the first position, and the method may include the following steps:
41. the processor acquires a preset cell map;
42. acquiring the intimacy between each other object in the m other objects and the first object to obtain m intimacy;
43. determining a maximum affinity of the m affinities;
44. and generating a target action line overlapped with the selected reference action track according to the reference action track corresponding to the maximum affinity in the m reference action tracks and the preset cell map.
The intimacy between each other object in the m other objects and the first object may be preset, specifically, the user may set the intimacy between the first person and the other person through the terminal device in advance, or set the intimacy between the first animal and the other animal in advance, and report the set intimacy to the intelligent line recommendation device, so that the intelligent line recommendation device may obtain the intimacy between the multiple objects in the cell in advance, and obtain multiple intimacy. Therefore, in the process of pushing the intelligent circuit to the first object, the processor can acquire the intimacy between each other object in the m other objects and the first object, and obtain m intimacy.
Referring to fig. 2B, fig. 2B is a schematic illustration of intelligent line recommendation provided in an embodiment of the present invention, in the embodiment of the present invention, it is assumed that a first object is a first person, and the m other objects are m persons, so that the affinity between the first person and each of the m other persons can be determined, m affinities are obtained, the maximum affinity of the m affinities is determined, the reference action track of the target other person corresponding to the maximum affinity of the m reference action tracks is generated according to the reference action track of the m reference action tracks, and a target action line overlapping with the selected reference action track is generated according to a preset cell map, so that a close and personalized action line can be pushed for a user more intelligently.
Optionally, if the first object is a first person, the m other objects are m persons, the affinity may also be an affinity of the first person and other m persons in the social application, specifically, a friend relationship exists between the first person and a part of the other m persons, and no friend relationship exists between the first person and another part of the other m persons, so that the affinity of the first person and a part of the other m persons is higher, the affinity on the social application may also be obtained, and the affinity between the part of the first person and the first person may be obtained, and the affinity between the first person and another part of the other m persons may be set to 0.
In a specific implementation, if the first object is a first animal, the m other objects are m animals, or the first object is a first person, the m other objects are m persons, the intimacy between each other object in the m other objects and the first object can be obtained, and the m intimacy is obtained, further, a target action line overlapped with the selected reference action track is generated according to a preset cell map, and the target action line is further obtained according to the reference action track corresponding to the maximum intimacy in the m reference action tracks.
According to the intelligent line recommendation method, the obtained multiple shot videos are subjected to video analysis through the processor to obtain multiple video image sets, the artificial intelligent chip sequentially calculates video images in the multiple video image sets according to a preset artificial intelligent algorithm to obtain a first position where a first object is currently located in a cell and m other positions where m other objects are located, the predictor predicts m reference action tracks of the m other objects according to the m other positions of the m other objects and the multiple action tracks stored in the memory in advance, and the processor recommends a target action line for the first object according to the m reference action tracks and the first position, so that the target action line can be pushed for the first object according to the m reference action tracks of the other objects in the cell, the processing speed of the video images is improved, the route recommendation can be rapidly and effectively performed, the recommended travelling route of the first object can be reasonably planned and adjusted according to the m other objects, and the activity experience of a user in the cell is improved.
In accordance with fig. 2A, please refer to fig. 3, which is a schematic flow chart of an embodiment of an intelligent circuit recommendation method according to an embodiment of the present invention. The intelligent line recommending method described in the present embodiment is applied to the intelligent line recommending apparatus shown in fig. 1B, and the method includes the following steps:
301. the processor analyzes the acquired multiple shot videos to obtain multiple video image sets, wherein the multiple shot videos are shot videos of different areas of a cell in a first preset time period.
302. The artificial intelligent chip sequentially calculates the video images in the plurality of video image sets according to a preset artificial intelligent algorithm to obtain a first position where a first object is currently located in the cell and m other positions where m other objects are located, wherein m is a positive integer, each other object corresponds to one other position, and the first position and the m other positions are located in different areas in the cell.
303. The predictor predicts m reference action trajectories of the m other objects according to m other positions of the m other objects and a plurality of action trajectories stored in the memory in advance.
304. The first object is a first person, the m other objects are m animals, and the processor acquires a preset cell map; and acquiring a preset user tag of the first person.
305. And if the preset user tag meets a preset condition, generating a target action line with the minimum intersection with the m reference action tracks according to the preset cell map.
The user tag may be determined according to usage data of a user using the terminal device, for example, the user may use a mobile phone, the user tag may be determined according to usage data of the user using the mobile phone, and the user tag may also be a tag set by the user. The user tag may be, for example, a favorite animal, a non-favorite animal, an animal-fear, a dog with a large size, or the like, and is not limited thereto. Therefore, when the preset user tag is a dog with a big fear body, the target action line without the dog with the big body size can be pushed for the user.
The preset user tag satisfies a preset condition, for example, the preset user tag may be a favorite animal, fear animal, or the like. In a specific implementation, if the first object is a first person, the m other objects are m animals, the preset user tag is a non-favorite animal or an animal fear, and the target action line with the minimum intersection with the m reference action tracks can be generated according to the preset cell map, so that the activity experience of the user in the cell can be improved.
The specific description of the steps 301 to 305 may refer to the corresponding description of the intelligent circuit recommendation method described in fig. 2A, and will not be repeated herein.
It can be seen that, according to the intelligent line recommendation method described in the embodiment of the present invention, a plurality of acquired shot videos are subjected to video analysis by a processor to obtain a plurality of video image sets, an artificial intelligence chip sequentially calculates video images in the plurality of video image sets according to a preset artificial intelligence algorithm to obtain a first position where a first object is currently located in the cell and m other positions where m other objects are located, a predictor predicts m reference action tracks of the m other objects according to the m other positions of the m other objects and a plurality of action tracks prestored in a memory, and the processor obtains a preset cell map; acquiring a preset user tag of a first person; if the preset user tag meets the preset condition, generating a target action line with the minimum intersection with m reference action tracks according to a preset cell map, so that the target action line can be pushed for the first object according to m reference action tracks of other objects in the cell, the processing speed of video images is improved, and route recommendation can be rapidly and effectively carried out, so that the recommended travel route of the first object can be reasonably planned and adjusted according to other m objects, and further the activity experience feeling of the user in the cell is improved.
The following is a device for implementing the intelligent line recommendation method, which specifically comprises the following steps:
in accordance with the foregoing, referring to fig. 4, fig. 4 is a schematic structural diagram of an intelligent circuit recommendation device according to an embodiment of the present invention, where the intelligent circuit recommendation device includes: processor 410, memory 420, artificial intelligence chip 430, and predictor 440; and one or more programs 421, the one or more programs 421 stored in the memory and configured to be executed by the processor, artificial intelligence chip, and predictor, the programs 421 comprising instructions for performing the steps of:
the acquired multiple shot videos are subjected to video analysis to obtain multiple video image sets, wherein the multiple shot videos are shot videos of different areas of a cell in a first preset time period;
sequentially calculating video images in the plurality of video image sets according to a preset artificial intelligence algorithm to obtain a first position where a first object is currently located in the cell and m other positions where m other objects are located, wherein m is a positive integer, each other object corresponds to one other position, and the first position and the m other positions are located in different areas in the cell;
Predicting m reference action tracks of the m other objects according to m other positions of the m other objects and a plurality of action tracks stored in the memory in advance;
and recommending a target action line for the first object according to the m reference action tracks and the first position.
In one possible example, the preset artificial intelligence algorithm includes a direction gradient histogram algorithm, and in the aspect that the video images in the plurality of video image sets are sequentially operated according to the preset artificial intelligence algorithm to obtain a first position in the cell where the first object is currently located, the program 421 includes instructions for performing the following steps:
converting a first video image of the plurality of video image sets into first input data and transmitting the first input data to a data access circuit;
transmitting the first input data to an arithmetic circuit; the data access circuit reads an operation instruction from the register circuit and sends the operation instruction to the operation circuit;
performing pixel gradient calculation according to the operation instruction and the first input data to obtain gradient magnitudes and gradient directions of all pixel points in the first video image; and counting the gradient amplitude values and gradient directions of all the pixel points to obtain a feature vector set belonging to the first object in the first video image, and determining a first position where the first object is currently located according to the feature vector set.
In one possible example, before the artificial intelligence chip sequentially performs operations on the video images in the plurality of video image sets according to a preset artificial intelligence algorithm, the program 421 further includes instructions for performing the following steps:
determining the similarity among a plurality of video images in a first preset duration in each video image set to obtain at least one similarity set;
taking a plurality of video images with the similarity larger than a preset threshold value in each similarity set in the at least one similarity set as a group of repeated video images to obtain at least one group of repeated video images;
performing quality evaluation on each of the at least one set of repeated video images to obtain at least one set of quality evaluation values;
selecting a maximum quality evaluation value in each group of quality evaluation values in the at least one group of quality evaluation values to obtain at least one maximum quality evaluation value;
and selecting video images corresponding to each maximum quality evaluation value in the at least one maximum quality evaluation value in the plurality of video image sets, and obtaining a plurality of screened video images.
In one possible example, the program 421 further includes instructions for performing the steps of:
Acquiring a plurality of historical videos of different areas of the cell within a second preset time period;
performing object recognition according to the plurality of historical videos to obtain a plurality of historical recognition results of a plurality of objects, wherein each historical recognition result comprises a position corresponding to one object, and each historical recognition result corresponds to one shooting time;
determining a plurality of action tracks according to the plurality of historical identification results and a plurality of shooting times corresponding to the plurality of historical identification results one by one, wherein each action track is an action track of an object in the second preset time period;
and storing the action tracks to the memory.
In one possible example, the first object is a first animal, the m other objects are m animals, or the first object is a first person, the m other objects are m persons, and the program 421 includes instructions for performing the following steps in recommending a target course of action for the first object according to the m reference courses of action and the first location:
acquiring a preset cell map;
acquiring the intimacy between each other object in the m other objects and the first object to obtain m intimacy;
Determining a maximum affinity of the m affinities;
and generating a target action line overlapped with the selected reference action track according to the reference action track corresponding to the maximum affinity in the m reference action tracks and the preset cell map.
In one possible example, the first object is a first person, the m other objects are m animals, and the program 421 includes instructions for performing the following steps in recommending a target course of action for the first object according to the m reference courses of action and the first location:
acquiring a preset cell map; acquiring a preset user tag of the first person;
and if the preset user tag meets a preset condition, generating a target action line with the minimum intersection with the m reference action tracks according to the preset cell map.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an intelligent circuit recommendation device according to the present embodiment, the intelligent circuit recommendation device 500 described in the present embodiment includes a processor 501, an artificial intelligent chip 502, a predictor 503 and a memory 504, wherein,
the processor is used for carrying out video analysis on the acquired multiple shot videos to obtain multiple video image sets, wherein the multiple shot videos are shot videos of different areas of a cell in a first preset time period;
The artificial intelligent chip is used for sequentially calculating the video images in the plurality of video image sets according to a preset artificial intelligent algorithm to obtain a first position where a first object is currently located in the cell and m other positions where m other objects are located, wherein m is a positive integer, each other object corresponds to one other position, and the first position and the m other positions are located in different areas in the cell;
the predictor is used for predicting m reference action tracks of the m other objects according to m other positions of the m other objects and a plurality of action tracks stored in the memory in advance;
the processor is further configured to recommend a target action route for the first object according to the m reference action tracks and the first position.
Optionally, the preset artificial intelligence algorithm includes a direction gradient histogram algorithm, the artificial intelligence chip includes a data converter, a data access circuit, a register circuit, and an operation circuit, where the operation is sequentially performed on the video images in the plurality of video image sets by the artificial intelligence chip according to the preset artificial intelligence algorithm to obtain a first position aspect where a first object is currently located in the cell,
The data converter is used for converting a first video image in the plurality of video image sets into first input data and transmitting the first input data to the data access circuit;
the data access circuit is used for sending the first input data to the operation circuit; the data access circuit reads an operation instruction from the register circuit and sends the operation instruction to the operation circuit;
the operation circuit is used for carrying out pixel gradient calculation according to the operation instruction and the first input data to obtain gradient amplitude values and gradient directions of all pixel points in the first video image; and counting the gradient amplitude values and gradient directions of all the pixel points to obtain a feature vector set belonging to the first object in the first video image, and determining a first position where the first object is currently located according to the feature vector set.
Optionally, before the artificial intelligence chip sequentially performs operations on the video images in the plurality of video image sets according to a preset artificial intelligence algorithm, the processor is further configured to:
determining the similarity among a plurality of video images in a first preset duration in each video image set to obtain at least one similarity set;
Taking a plurality of video images with the similarity larger than a preset threshold value in each similarity set in the at least one similarity set as a group of repeated video images to obtain at least one group of repeated video images;
performing quality evaluation on each of the at least one set of repeated video images to obtain at least one set of quality evaluation values;
selecting a maximum quality evaluation value in each group of quality evaluation values in the at least one group of quality evaluation values to obtain at least one maximum quality evaluation value;
and selecting video images corresponding to each maximum quality evaluation value in the at least one maximum quality evaluation value in the plurality of video image sets, and obtaining a plurality of screened video images.
Optionally, the processor is further configured to:
acquiring a plurality of historical videos of different areas of the cell within a second preset time period;
performing object recognition according to the plurality of historical videos to obtain a plurality of historical recognition results of a plurality of objects, wherein each historical recognition result comprises a position corresponding to one object, and each historical recognition result corresponds to one shooting time;
determining a plurality of action tracks according to the plurality of historical identification results and a plurality of shooting times corresponding to the plurality of historical identification results one by one, wherein each action track is an action track of an object in the second preset time period;
And storing the action tracks to the memory.
Optionally, the first object is a first animal, the m other objects are m animals, or the first object is a first person, the m other objects are m persons, and the processor is specifically configured to, in the aspect that the processor recommends a target action route for the first object according to the m reference action tracks and the first position:
acquiring a preset cell map;
acquiring the intimacy between each other object in the m other objects and the first object to obtain m intimacy;
determining a maximum affinity of the m affinities;
and generating a target action line overlapped with the selected reference action track according to the reference action track corresponding to the maximum affinity in the m reference action tracks and the preset cell map.
Optionally, the first object is a first person, the m other objects are m animals, and in the aspect that the processor recommends a target action route for the first object according to the m reference action tracks and the first position, the processor is specifically configured to:
acquiring a preset cell map; acquiring a preset user tag of the first person;
And if the preset user tag meets a preset condition, generating a target action line with the minimum intersection with the m reference action tracks according to the preset cell map.
It can be seen that, according to the intelligent line recommendation device described in the embodiment of the present invention, the obtained multiple shot videos are subjected to video analysis by the processor to obtain multiple video image sets, the artificial intelligence chip sequentially calculates video images in the multiple video image sets according to a preset artificial intelligence algorithm to obtain a first position where a first object is currently located in the cell and m other positions where m other objects are located, the predictor predicts m reference action tracks of the m other objects according to the m other positions of the m other objects and the multiple action tracks stored in the memory in advance, and the processor recommends a target action line for the first object according to the m reference action tracks and the first position, so that the target action line can be pushed for the first object according to the m reference action tracks of the other objects in the cell, so that the recommended running line of the first object can be reasonably planned and adjusted according to the m other objects, and further the activity experience of the user in the cell is improved.
It may be understood that the functions of each program module of the intelligent line recommendation apparatus of the present embodiment may be specifically implemented according to the method in the foregoing method embodiment, and the specific implementation process may refer to the relevant description of the foregoing method embodiment, which is not repeated herein.
The embodiment of the invention also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program makes a computer execute part or all of the steps of any one of the methods described in the embodiment of the method, and the computer includes an intelligent line recommendation device.
Embodiments of the present invention also provide a computer program product comprising a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform part or all of the steps of any one of the methods described in the method embodiments above. The computer program product may be a software installation package, said computer comprising intelligent line recommendation means.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present invention is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present invention. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present invention.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, such as the above-described division of units, merely a division of logic functions, and there may be additional manners of dividing in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present invention may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned method of the various embodiments of the present invention. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
The foregoing has outlined rather broadly the more detailed description of embodiments of the invention, wherein the principles and embodiments of the invention are explained in detail using specific examples, the above examples being provided solely to facilitate the understanding of the method and core concepts of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.

Claims (10)

1. An intelligent line recommendation method, applied to an intelligent line recommendation device, the device comprising a processor, an artificial intelligent chip, a predictor and a memory, the method comprising:
the processor analyzes the acquired multiple shot videos to obtain multiple video image sets, wherein the multiple shot videos are shot videos of different areas of a cell in a first preset time period;
The artificial intelligent chip sequentially calculates video images in the plurality of video image sets according to a preset artificial intelligent algorithm to obtain a first position where a first object is currently located in the cell and m other positions where m other objects are located, wherein m is a positive integer, each other object corresponds to one other position, and the first position and the m other positions are located in different areas in the cell;
the predictor predicts m reference action tracks of the m other objects according to m other positions of the m other objects and a plurality of action tracks stored in the memory in advance;
the processor recommends a target action line for the first object according to the m reference action tracks and the first position.
2. The method according to claim 1, wherein the artificial intelligence chip includes a data converter, a data access circuit, a register circuit, and an operation circuit, and the sequentially operating the video images in the plurality of video image sets by the artificial intelligence chip according to a preset artificial intelligence algorithm to obtain a first location in the cell where the first object is currently located includes:
The data converter converts a first video image in the plurality of video image sets into first input data and transmits the first input data to a data access circuit;
the data access circuit sends the first input data to the operation circuit; the data access circuit reads an operation instruction from the register circuit and sends the operation instruction to the operation circuit;
the operation circuit performs pixel gradient calculation according to the operation instruction and the first input data to obtain gradient magnitudes and gradient directions of all pixel points in the first video image; and counting the gradient amplitude values and gradient directions of all the pixel points to obtain a feature vector set belonging to the first object in the first video image, and determining a first position where the first object is currently located according to the feature vector set.
3. The method of claim 1 or 2, wherein before the artificial intelligence chip sequentially operates on the video images in the plurality of video image sets according to a preset artificial intelligence algorithm, the method further comprises:
the processor determines the similarity among a plurality of video images in a first preset duration in each video image set to obtain at least one similarity set;
Taking a plurality of video images with the similarity larger than a preset threshold value in each similarity set in the at least one similarity set as a group of repeated video images to obtain at least one group of repeated video images;
performing quality evaluation on each of the at least one set of repeated video images to obtain at least one set of quality evaluation values;
selecting a maximum quality evaluation value in each group of quality evaluation values in the at least one group of quality evaluation values to obtain at least one maximum quality evaluation value;
and selecting video images corresponding to each maximum quality evaluation value in the at least one maximum quality evaluation value in the plurality of video image sets, and obtaining a plurality of screened video images.
4. A method according to any one of claims 1-3, wherein the method further comprises:
the processor acquires a plurality of historical videos of different areas of the cell within a second preset time period;
performing object recognition according to the plurality of historical videos to obtain a plurality of historical recognition results of a plurality of objects, wherein each historical recognition result comprises a position corresponding to one object, and each historical recognition result corresponds to one shooting time;
Determining a plurality of action tracks according to the plurality of historical identification results and a plurality of shooting times corresponding to the plurality of historical identification results one by one, wherein each action track is an action track of an object in the second preset time period;
and storing the action tracks to the memory.
5. The method of any of claims 1-4, wherein the first object is a first animal, the m other objects are m animals, or the first object is a first person, the m other objects are m persons, and the processor recommends a target course of action for the first object based on the m reference courses of action and the first location, comprising:
the processor acquires a preset cell map;
acquiring the intimacy between each other object in the m other objects and the first object to obtain m intimacy;
determining a maximum affinity of the m affinities;
and generating a target action line overlapped with the selected reference action track according to the reference action track corresponding to the maximum affinity in the m reference action tracks and the preset cell map.
6. The method of any of claims 1-4, wherein the first object is a first person and the m other objects are m animals, the processor recommending a target course of action for the first object based on the m reference courses of action and the first location, comprising:
the processor acquires a preset cell map; acquiring a preset user tag of the first person;
and if the preset user tag meets a preset condition, generating a target action line with the minimum intersection with the m reference action tracks according to the preset cell map.
7. An intelligent line recommendation device, characterized in that the device comprises a processor, an artificial intelligent chip, a predictor and a memory, wherein,
the processor is used for carrying out video analysis on the acquired multiple shot videos to obtain multiple video image sets, wherein the multiple shot videos are shot videos of different areas of a cell in a first preset time period;
the artificial intelligent chip is used for sequentially calculating the video images in the plurality of video image sets according to a preset artificial intelligent algorithm to obtain a first position where a first object is currently located in the cell and m other positions where m other objects are located, wherein m is a positive integer, each other object corresponds to one other position, and the first position and the m other positions are located in different areas in the cell;
The predictor is used for predicting m reference action tracks of the m other objects according to m other positions of the m other objects and a plurality of action tracks stored in the memory in advance;
the processor is further configured to recommend a target action route for the first object according to the m reference action tracks and the first position.
8. The apparatus of claim 7, wherein the artificial intelligence chip comprises a data converter, a data access circuit, a register circuit, and an operation circuit, wherein the operation is sequentially performed on the video images in the plurality of video image sets by the artificial intelligence chip according to a preset artificial intelligence algorithm to obtain a first position aspect of a first object in the cell,
the data converter is used for converting a first video image in the plurality of video image sets into first input data and transmitting the first input data to the data access circuit;
the data access circuit is used for sending the first input data to the operation circuit; the data access circuit reads an operation instruction from the register circuit and sends the operation instruction to the operation circuit;
The operation circuit is used for carrying out pixel gradient calculation according to the operation instruction and the first input data to obtain gradient amplitude values and gradient directions of all pixel points in the first video image; and counting the gradient amplitude values and gradient directions of all the pixel points to obtain a feature vector set belonging to the first object in the first video image, and determining a first position where the first object is currently located according to the feature vector set.
9. A smart line recommendation device comprising a processor, an artificial intelligence chip, a predictor and a memory, the memory for storing one or more programs and configured to be executed by the processor, the artificial intelligence chip and the predictor, the programs comprising instructions for performing the steps in the method of any of claims 1-6.
10. A computer-readable storage medium, characterized in that a computer program for electronic data exchange is stored, wherein the computer program causes a computer to perform the method according to any one of claims 1-6.
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