WO2019051814A1 - Procédé et appareil de reconnaissance de cible, et terminal intelligent - Google Patents

Procédé et appareil de reconnaissance de cible, et terminal intelligent Download PDF

Info

Publication number
WO2019051814A1
WO2019051814A1 PCT/CN2017/101967 CN2017101967W WO2019051814A1 WO 2019051814 A1 WO2019051814 A1 WO 2019051814A1 CN 2017101967 W CN2017101967 W CN 2017101967W WO 2019051814 A1 WO2019051814 A1 WO 2019051814A1
Authority
WO
WIPO (PCT)
Prior art keywords
tested
result
recognition
confidence
information
Prior art date
Application number
PCT/CN2017/101967
Other languages
English (en)
Chinese (zh)
Inventor
廉士国
刘兆祥
王宁
Original Assignee
达闼科技（北京）有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 达闼科技（北京）有限公司 filed Critical 达闼科技（北京）有限公司
Priority to PCT/CN2017/101967 priority Critical patent/WO2019051814A1/fr
Priority to CN201780002292.4A priority patent/CN107995982B/zh
Publication of WO2019051814A1 publication Critical patent/WO2019051814A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/94—Hardware or software architectures specially adapted for image or video understanding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/07—Target detection

Definitions

  • the embodiments of the present invention relate to the field of intelligent identification technologies, and in particular, to a target identification method, apparatus, and intelligent terminal.
  • the environment is variable, and the recognition capability of the smart terminal is limited.
  • the smart terminal may not be able to accurately identify the target to be tested, for example, due to light, angle, or occlusion. Identify who this person is, and for example, because of the distance and angle, you can't be sure of the brand or model of the car.
  • the intelligent terminal if it is required to output a higher-level recognition result, it may cause embarrassment due to the recognition error; or, if the output result is discarded because the detailed result is not obtained, Not conducive to a user-friendly experience.
  • the embodiment of the present application provides a target recognition method, device, and intelligent terminal, which can solve the problem of how to achieve a compromise between reliability and detail level of target recognition.
  • an embodiment of the present application provides a target identification method, which is applied to an intelligent terminal, and includes include:
  • Collecting information for the object to be tested includes at least two types of attributes, and a priority relationship is set between the at least two types of attributes;
  • the recognition result of the object to be tested is a determination result corresponding to one of the attribute types, the confidence of the determination result satisfies a preset condition, and the recognition result corresponds to
  • the attribute type has the highest priority among the attribute types corresponding to the determination result that the confidence degree satisfies the preset condition.
  • an embodiment of the present application provides a target identification apparatus, including:
  • An information collecting unit configured to collect information about a target to be tested, where the object to be tested includes at least two types of attributes, and a priority relationship is set between the at least two types of attributes;
  • a recognition unit configured to output a recognition result of the object to be tested based on the information, where the recognition result is a determination result corresponding to one of the attribute types, the confidence of the determination result satisfies a preset condition, and The attribute type corresponding to the recognition result has the highest priority among the attribute types corresponding to the determination result that the confidence degree satisfies the preset condition.
  • an intelligent terminal including:
  • At least one processor and,
  • the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the target recognition method as described above.
  • an embodiment of the present application provides a non-transitory computer readable storage medium, where the non-transitory computer readable storage medium stores computer executable instructions for causing a smart terminal to execute the above The target recognition method.
  • the embodiment of the present application further provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, when the program When the instruction is executed by the smart terminal, causing the smart terminal to execute as above The target recognition method.
  • the target identification method, apparatus, and intelligent terminal provided by the embodiments of the present application divide multiple attributes with priority order for the attributes of the target to be tested according to the degree of detail of the description of the object to be tested.
  • Type and in the process of identification, obtain the confidence of the judgment result under each attribute type, and then output the judgment result corresponding to the attribute type with the highest priority among the judgment results satisfying the preset condition according to the actual recognition situation.
  • the recognition result of the target to be measured can ensure the reliability of the output recognition result under different recognition scenarios, and at the same time, output a more detailed recognition result as much as possible, that is, the final recognition result can be reliability and A compromise between levels of detail improves the user experience.
  • FIG. 1 is a schematic flowchart diagram of a target recognition method according to an embodiment of the present application
  • FIG. 2 is a schematic flow chart of another object recognition method provided by an embodiment of the present application.
  • FIG. 3 is a schematic structural diagram of a target recognition apparatus according to an embodiment of the present application.
  • FIG. 4 is a schematic structural diagram of hardware of an intelligent terminal according to an embodiment of the present application.
  • the embodiment of the present application provides a target recognition method, device, and intelligent terminal, which can be applied to any application field related to target recognition, such as: intelligent guide blind, welcome robot, service robot, intrusion object detection, semantic recognition, and the like.
  • the target recognition method provided by the embodiment of the present application is an intelligent optimization identification method based on the “priority” of the attribute type of the target to be measured and the “confidence” of the determination result under each attribute type, according to the
  • the degree of detail of the description of the target is divided into a plurality of attribute types having priority order for the attribute of the target to be tested (where the higher the priority type, the higher the degree of detail corresponding to the judgment result), and in the process of identification
  • a confidence level for evaluating the reliability of the judgment result is set, and then the judgment corresponding to the attribute type having the highest priority among the judgment results satisfying the preset condition is output according to the actual recognition situation.
  • the target recognition method and device and the intelligent terminal provided by the embodiment of the present application identify the same person/object (the object to be tested), different levels of recognition results may be output under different recognition environments. For example, in the case of identifying a person, when the light is good, the distance is close, and the person being tested is facing the machine camera, the "person name" of the person to be tested can be identified; when the person being tested covers half of the face by hand, or sideways When the machine camera is used, only the "gender” of the person being tested can be identified; when the person being tested is facing the camera of the machine, only whether it is a "person” can be identified.
  • the object recognition method and apparatus provided by the embodiments of the present application can be applied to any type of smart terminal, such as a robot, a guide glasses, a smart helmet, a smart phone, a tablet computer, a server, and the like.
  • the smart terminal can include any suitable type of storage medium for storing data, such as a magnetic disk, a compact disc (CD-ROM), a read-only memory or a random access memory.
  • the smart terminal may also include one or more logical computing modules that perform any suitable type of function or operation in parallel, such as viewing a database, image processing, etc., in a single thread or multiple threads.
  • the logic operation module may be any suitable type of electronic circuit or chip-type electronic device capable of performing logical operation operations, such as a single core processor, a multi-core processor, a graphics processing unit (GPU), or the like.
  • FIG. 1 is a schematic flowchart of a target identification method according to an embodiment of the present application. Referring to FIG. 1, the method includes but is not limited to:
  • the target to be tested may include, but is not limited to, a person, an animal, an object, and the like. According to the degree of detail of the description of the object to be measured, at least two different levels of attribute types may be divided for the object to be tested, and priority relationships are set for the attribute types according to the level of detail.
  • the attribute type with difficulty in identifying is relatively high in detail, and the difficulty level of recognition can be sorted according to the recognition rate of different attribute types under the same conditions (for example, inputting the same picture) (for example)
  • person name recognition is difficult to identify by gender, gender recognition is difficult for face/human recognition); or, it can be sorted according to the mutual inclusion relationship between attribute types (for example, to identify the gender, the presence of the face must be recognized first).
  • the attribute type of the person may be set according to the degree of detail of the description of the object to be tested, including: "person name”, “gender”, and “whether it is a person”, and according to the difficulty level of recognition, You can set the priority order of these attribute types to be: L1 (person name) > L2 (gender) > L3 (whether it is human).
  • the attribute types of the vehicle can be set to include: “a license plate”, “a model of the vehicle”, “the color of the vehicle”, and “whether or not the vehicle”, and according to the difficulty level of the identification, Set the priority order of these attribute types to: L1 (vehicle license plate) > L2 (model of the car) > L3 (color of the car) > L4 (whether it is a car).
  • the “information” may be any judgment basis that can reflect the attribute of the object to be tested, and the type of the information may include, but is not limited to, image information, sound information, thermal infrared image, near-infrared image. , ultrasonic signals, electromagnetic reflection signals, etc.
  • information about the object to be tested may be collected by one or more sensors, for example, collecting image information for the object to be tested through the camera, collecting sound information for the object to be tested through the microphone, and passing the thermal infrared.
  • the sensor collects a thermal infrared image or the like for the target to be tested.
  • each attribute type of the object to be tested corresponds to a determination result
  • each determination result corresponds to a reliable one for characterizing the determination result.
  • Confidence in sexuality or credibility.
  • the judgment results obtained by the target to be tested include: “Zhang San” (confidence is 70%), “male” (confidence is 89%), “person” ( The confidence level is 100%), then, “Zhang San”, “Men” and “People” are the judgment results corresponding to the attribute types "person name", "gender” and "whether or not” of the object to be tested.
  • the confidence level of the judgment result can be determined by the similarity degree of the feature comparison, and the higher the degree of similarity, the higher the confidence degree.
  • the outputting result is a determination result corresponding to one of the attribute types of the object to be tested, the confidence of the determination result satisfies a preset condition, and the attribute type corresponding to the recognition result is in confidence.
  • the priority of the attribute type corresponding to the judgment result satisfying the preset condition is the highest.
  • the “preset condition” may be set according to an actual application scenario, and used to identify the reliability of a certain judgment result.
  • the preset condition may be: a confidence level of the determination result is greater than or equal to a confidence threshold corresponding to the corresponding attribute type.
  • the confidence threshold corresponding to each attribute type may be the same.
  • the confidence thresholds corresponding to the attribute types “person name”, “gender”, and “whether or not” are both 70%, and the judgment result of the target to be tested is obtained. Including: “Zhang San” (confidence is 70%), “male” (confidence is 89%), "person” (confidence is 100%), then, the judgment results “Zhang San", "male” and " The confidence degree of the person meets the preset condition.
  • the recognition result of the object to be tested is the judgment result "Zhang San” corresponding to the attribute type "person name” having the highest priority among the three.
  • the confidence threshold corresponding to each attribute type may also be different.
  • the confidence threshold corresponding to the attribute type “person name” may be preset to be 75%, corresponding to the attribute type “gender”. The confidence threshold is 85%, and the confidence threshold corresponding to the attribute type “is it human” is 95%. If the target to be tested is the same, “Zhang San” (confidence is 70%), “male” ( Confidence is 89%), "People” (confidence is 100%), then the judgment result that the confidence meets the preset condition includes only "male” and "person”. At this time, the recognition result of the target to be tested is The highest priority attribute type "gender” corresponds to the judgment result "male”.
  • the identification result of the target to be tested is output based on the collected information.
  • Embodiments may include, but are not limited to, the following two embodiments:
  • the determination result corresponding to each attribute type of the object to be tested and the confidence of each determination result may be firstly obtained based on the collected information; and then the priority of the determination result that the confidence degree satisfies the preset condition is output.
  • the judgment result corresponding to the highest attribute type is used as the recognition result of the object to be tested.
  • the determination result corresponding to each attribute type of the object to be tested based on the collected information may be implemented by using a suitable algorithm (for example, a neural network). For example, if the target to be measured is a person and the information collected by the smart terminal is the image information of the person, the smart terminal may iteratively calculate the attribute type “whether it is a person”, “gender” and “person name” from the image.
  • a suitable algorithm for example, a neural network
  • the judgment result for example, firstly calculates the feature 1 for discriminating whether it is a person by the bottom layer of the neural network, and obtains the judgment result of "whether it is a person” according to the feature 1 and the confidence of the judgment result; then, in the neural network The middle layer calculates feature 2 for discriminating "gender” based on feature 1, and obtains the judgment result corresponding to "gender” according to feature 2 and the confidence of the judgment result; finally, based on feature 2 at the uppermost layer of the neural network
  • the feature 3 for discriminating the "person name” is obtained, and according to the feature 3, the judgment result corresponding to the "person name” and the confidence of the judgment result are obtained.
  • the judgment result that the confidence degree satisfies the preset requirement is first screened, and then the judgment of the target to be tested is selected to the highest degree of detail (that is, the corresponding attribute type has the highest priority). The result is the recognition result of the target to be tested.
  • the determination result corresponding to each attribute type of the target to be tested and the confidence of each determination result may be obtained step by step according to the priority from high to low, until the first
  • the determination result that the confidence degree satisfies the preset condition appears the determination result that the first confidence degree satisfies the preset condition is output as the recognition result of the object to be tested. That is, when the information for the object to be tested is collected, firstly, based on the collected information, the first-level judgment result corresponding to the attribute type with the highest priority and the first-level confidence level of the first-level judgment result are obtained, if the first-level confidence is satisfied.
  • the preset condition for example, the first-level confidence is greater than or equal to the first-level confidence threshold
  • the first-level judgment result is directly output as the recognition result of the target to be tested, otherwise, the attribute level corresponding to the next level is obtained based on the collected information.
  • the second-level judgment result and the second-level confidence of the second-level judgment result if the second-level confidence level satisfies a preset condition (for example, the second-level confidence level is greater than or equal to the second-level confidence threshold), the second-level judgment result is output As a result of the identification of the target to be tested, otherwise, the determination result corresponding to the attribute type of the next level and the confidence thereof are obtained based on the collected information, and the loop is repeated until the determination result that the confidence level satisfies the preset condition is obtained.
  • a preset condition for example, the second-level confidence level is greater than or equal to the second-level confidence threshold
  • different features may be extracted from the collected information for different levels of judgment. For example, if the target to be measured is a vehicle and the collected information is image information for the vehicle, The feature a can be extracted from the image information for identifying whether there is a car in the image, the feature b is extracted for identifying the color of the car in the image, and the feature c is extracted for identifying the type of the car (car, truck, bus, etc.) Wait.
  • the judgment result corresponding to each attribute type of the object to be tested and the confidence thereof are obtained step by step according to the order of priority from high to low, when the first confidence level satisfies the judgment result of the preset condition Directly outputting the judgment result that the first confidence degree satisfies the preset condition, without identifying and judging each attribute type, thereby reducing the amount of data processing, and improving the recognition without affecting the level of detail and reliability. effectiveness.
  • the target recognition method may further include: transmitting an interaction signal corresponding to the recognition result.
  • the smart glasses or smart helmet for guiding blind can send a voice prompt “Your friend Zhang San” to the user for welcoming or providing service.
  • the robot can say "Hello! VIP customer Zhang San!” to the target, and/or adjust the gestures exclusive to VIP customers.
  • the recognition result outputted in step 120 is “male”
  • the smart glasses or the smart helmet for guiding the blind can give the user a voice prompt “there is a man in front”, and the robot for welcoming or providing the service can be tested.
  • the goal is "Hello! Sir!.
  • the object recognition method provided by the embodiment of the present application is that the target identification method provided by the embodiment of the present application divides multiple attribute types with priority order for the attribute of the target to be tested according to the detailed level of the description of the object to be tested. And in the process of identification, get each attribute type The confidence of the result of the determination is determined, and then the judgment result corresponding to the attribute type having the highest priority among the judgment results satisfying the preset condition is output as the recognition result of the object to be tested according to the actual recognition situation, and can be in different recognition scenarios. The reliability of the recognition result of the output is ensured, and at the same time, the more detailed recognition result is output as much as possible, that is, the final recognition result can be compromised between reliability and detail level, thereby improving the user experience.
  • the second embodiment of the present application further provides another target identification method.
  • the collected information includes at least two types of information sources.
  • FIG. 2 includes but is not limited to:
  • the "information source” refers to a source of information capable of reflecting an attribute of a target to be tested.
  • the “at least two information sources” may be at least two different types of information, such as any two or more of image information, sound information, thermal infrared images, near infrared images, ultrasonic signals, or electromagnetic reflection signals;
  • the “at least two information sources” may also be some type of information collected from at least two angles or moments, for example, image information (or sound information) of the target to be measured is collected from multiple angles, and each Image information (or sound information) acquired from a single perspective can be used as a source of information.
  • the “at least two information sources” may also be a combination of the above two forms.
  • the information collected for the target to be tested includes image information collected from multiple angles and from one image. Sound information collected at an angle.
  • step 110 the specific implementation manner of collecting each information source may refer to step 110 in the foregoing embodiment 1, and will not be described in detail herein.
  • the recognition result of the target to be tested is obtained by means of multi-information fusion.
  • the specific implementation manner of outputting the recognition result of the object to be tested based on the collected at least two types of information sources may include, but is not limited to, the following three implementation manners:
  • the target to be tested may be identified by means of “sub-mode fusion”, that is, firstly, the sub-recognition results of the object to be tested are acquired based on the collected at least two information sources, and then the sub-identifications are determined according to the sub-identifications.
  • the result outputs the recognition result of the object to be tested.
  • the “sub-recognition result” refers to a recognition result obtained based on only one information source, and each information source corresponds to one sub-recognition result. Therefore, in this embodiment, the sub-recognition result also includes at least two, and each sub-recognition result has a corresponding confidence level for evaluating the reliability of the sub-recognition result.
  • the sub-recognition result corresponding to each information source may be separately obtained by the target identification method (shown in FIG. 1) provided in the first embodiment, and then selected from the sub-recognition results.
  • the most detailed sub-identification result is used as the recognition result of the object to be tested.
  • the level of detail of the sub-recognition result may be determined by the priority of the attribute type corresponding to the sub-recognition result, and the higher the priority of the corresponding attribute type, the higher the level of detail, for example, the obtained sub-recognition result includes “ “person” and "girl”, wherein the attribute type corresponding to the child recognition result "person” is "whether it is a person", the attribute type of the child recognition result "girl” is "gender”, and the priority of "gender” is higher than " If the person is a person, the sub-recognition result "girl” is higher in detail than the sub-recognition result "person”, so that the sub-recognition result "girl” can be used as the recognition result of the object to be tested.
  • the steps 110 to 120 in the first embodiment may be performed based on the collected image information; and the first embodiment is executed based on the collected sound information. Steps 110 to 120. It is assumed that the sub-recognition result output based on the acquired image information is “person”, and the sub-recognition result output based on the collected sound information is “Li Si”, and the sub-recognition result “Li Si” with higher level of detail can be output. "As the result of the identification of the target to be tested.
  • the sub-recognition result with the highest degree of confidence can be selected from the sub-identification result with the highest degree of detail as the recognition result of the object to be tested, for example, the confidence of the sub-recognition result “boy” is 70%, and the sub-recognition result “ The confidence of the girl is 90% (>70%), so that the sub-recognition result “girl” can be selected as the target of the object to be tested. result.
  • the level of detail of the target recognition can be further improved.
  • the target to be tested may be identified by means of a hierarchical decision fusion, that is, the test is obtained step by step according to the priority from the highest to the lowest based on the at least two information sources.
  • the sub-judgment result of the condition is used as the recognition result of the object to be tested.
  • the “sub-judgment result” refers to a judgment result of the object to be tested under a certain attribute type based on only one information source analysis, and each information source corresponds to one sub-judgment result under each attribute type. .
  • the “sub-confidence” refers to the degree of credibility of the sub-judgment result and is used to characterize the reliability of the sub-judgment result.
  • the target to be tested is a person
  • the attribute types include: “person name”, “gender” and “whether it is a person”
  • the priority relationship is: L1 (person name)>L2 (gender)>L3 (whether it is a person)
  • collection The obtained information includes image information and sound information.
  • the sub-judgment result corresponding to the “person name” and the confidence thereof are first obtained based on the image information and the sound information, respectively, and it is assumed that the sub-judgment result obtained based on the image information is “Li Si”.
  • the sub-judgment result "Li Si” is the sub-judgment result that the first sub-confidence satisfies the preset condition (meeting the first preset condition or the second preset condition).
  • the sub-judgment result with the highest confidence among the sub-judgment results may be selected as the recognition result of the target to be tested.
  • the target to be tested is hierarchically identified based on at least two information sources, respectively, as long as one of the information sources is analyzed to obtain an optimal recognition result (ie, the most reliable and detailed identification node). Therefore, the optimal recognition result can be directly output, and the efficiency of target recognition can be improved.
  • the target to be tested may be identified by using a “hierarchical fusion decision”, that is, the to-be-selected from the at least two information sources is sequentially stepped according to the order of priority from high to low. Measure the feature corresponding to each attribute type of the target, and obtain the judgment result corresponding to each attribute type and the confidence of the judgment result according to the feature corresponding to each attribute type; until the first confidence level satisfies the determination result of the preset condition When it appears, the judgment result that the first confidence level satisfies the preset condition is output as the recognition result of the object to be tested.
  • a “hierarchical fusion decision” that is, the to-be-selected from the at least two information sources is sequentially stepped according to the order of priority from high to low.
  • the target to be tested is a person
  • the attribute types include: “person name”, “gender” and “whether it is a person”, and the priority relationship is: L1 (person name)>L2 (gender)>L3 (whether it is a person), collection
  • the obtained information includes image information and sound information
  • the first-level feature A1 for identifying the "person name” may be first extracted from the collected image information, and the "person name” is extracted from the sound information.
  • the first-level feature A2 and then the two types of first-level features A1 and A2 are merged together (for example, A1 and A2 are spliced together by a neural network separator that combines two types of features) to generate feature A, and then identify according to feature A.
  • the first-level judgment result of the person name and the first-level confidence of the first-level judgment result if the first-level confidence level satisfies the first-level preset condition, the first-level judgment result is output; otherwise, the collected image information is extracted A secondary feature B1 for identifying "gender", and a secondary feature B2 for identifying "gender” are extracted from the sound information, and then the two types of secondary features B1 and B2 are fused together to generate feature B.
  • the second-level judgment result of “gender” and the second-level confidence of the second-level judgment result are recognized, and if the second-level confidence level satisfies the second-level preset condition, the second-level judgment result is output; otherwise, continue The judgment result corresponding to the attribute type of the next level and the confidence thereof are obtained, and the loop is obtained until the judgment result that the confidence degree satisfies the preset condition is obtained.
  • the determination information of the target recognition can be enriched, which not only can improve the detail level of the target recognition, but also improve the efficiency of the target recognition.
  • the object recognition method provided by the embodiment of the present application can improve the target identification method provided by the embodiment of the present application by collecting at least two types of information sources and outputting the recognition result of the object to be tested according to the at least two information sources.
  • the level of detail and efficiency of target recognition can improve the target identification method provided by the embodiment of the present application by collecting at least two types of information sources and outputting the recognition result of the object to be tested according to the at least two information sources.
  • FIG. 3 is a schematic structural diagram of a target recognition apparatus according to an embodiment of the present application.
  • the target identification apparatus 3 includes:
  • the information collecting unit 31 is configured to collect information about a target to be tested, where the object to be tested includes at least two types of attributes, and a priority relationship is set between the at least two types of attributes;
  • the identification unit 32 is configured to output a recognition result of the object to be tested based on the information, where the recognition result is a determination result corresponding to one of the attribute types, the confidence of the determination result satisfies a preset condition, and The attribute type corresponding to the recognition result has the highest priority among the attribute types corresponding to the determination result that the confidence degree satisfies the preset condition.
  • the recognition unit 32 when the information collecting unit 31 collects information for the object to be tested, the recognition unit 32 outputs the recognition result of the object to be tested based on the information.
  • the object to be tested includes at least two attribute types, and a priority relationship is set between the at least two attribute types; the recognition result is a determination result corresponding to one of the attribute types, and the determination result is The confidence level satisfies the preset condition, and the attribute type corresponding to the recognition result has the highest priority among the attribute types corresponding to the determination result that the confidence degree satisfies the preset condition.
  • the identifying unit 32 is specifically configured to: obtain, according to the information, a determination result corresponding to each attribute type of the object to be tested and a confidence level of each determination result; and output confidence that the preset condition meets the preset condition
  • the judgment result corresponding to the attribute type having the highest priority among the judgment results is used as the recognition result of the object to be tested.
  • the identification unit 32 includes an analysis module 321 and an output module 322.
  • the analysis module 321 is configured to acquire the information according to the target to be tested according to the priority from high to low. a determination result corresponding to each attribute type of the object to be tested and a confidence level of each determination result; the output module 322 is configured to output the first confidence when the first confidence level meets the preset condition The judgment result that satisfies the preset condition is used as the recognition result of the object to be tested.
  • the analysis module 321 is specifically configured to: according to the order of priority from high to low, And extracting, from the at least two information sources, the features corresponding to each attribute type of the object to be tested, and obtaining the determination result corresponding to each attribute type and the determination result according to the feature corresponding to each attribute type Confidence.
  • the identifying unit 32 is specifically configured to: based on the at least two information sources respectively, according to the priority from high to low.
  • the sub-judging result corresponding to each attribute type of the object to be tested and the sub-confidence of each sub-judgment result are obtained step by step until the sub-judgment result of the first sub-confidence satisfying the preset condition appears, and the output is a sub-judgment result that satisfies a preset condition as a recognition result of the object to be tested; or, a sub-recognition result of the object to be tested is acquired based on the at least two information sources, wherein each An information source corresponds to a sub-recognition result; and the recognition result of the object to be tested is output according to the sub-recognition result.
  • the target recognition device 3 further includes:
  • the interaction unit 33 is configured to send an interaction signal corresponding to the recognition result.
  • the object recognition apparatus divides the attribute type of the priority order into the attribute of the object to be tested according to the detailed degree of the description of the object to be tested. And in the process of identification, the confidence unit obtains the confidence of the judgment result under each attribute type, and then outputs the judgment result corresponding to the attribute type with the highest priority among the judgment results satisfying the preset condition according to the actual recognition situation.
  • the recognition result of the object to be tested it is possible to ensure the reliability of the output recognition result under different recognition scenarios, and at the same time, output a more detailed recognition result as much as possible, that is, the final recognition result can be reliably A compromise between sex and level of detail improves the user experience.
  • the 400 can be any type of smart terminal, such as a robot, a blind eyeglass, a smart helmet, a smart phone, a tablet, a server, etc., and can perform the target recognition method provided by the first embodiment and/or the second embodiment.
  • the smart terminal 400 includes:
  • One or more processors 401 and memory 402 are exemplified by one processor 401 in FIG.
  • the processor 401 and the memory 402 can be connected by a bus or other means, and the connection by a bus is taken as an example in FIG.
  • the memory 402 is used as a non-transitory computer readable storage medium, and can be used for storing a non-transitory software program, a non-transitory computer executable program, and a module, such as a program instruction/module corresponding to the target recognition method in the embodiment of the present application.
  • a module such as a program instruction/module corresponding to the target recognition method in the embodiment of the present application.
  • the processor 401 executes various functional applications and data processing of the target recognition device by executing non-transitory software programs, instructions, and modules stored in the memory 402, that is, implementing the target recognition method of any of the above method embodiments.
  • the memory 402 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function; the storage data area may store data created according to usage of the target identification device, and the like.
  • memory 402 can include high speed random access memory, and can also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device.
  • memory 402 can optionally include memory remotely located relative to processor 401, which can be connected to smart terminal 400 over a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
  • the one or more modules are stored in the memory 402, and when executed by the one or more processors 401, perform a target recognition method in any of the above method embodiments, for example, performing the above described FIG. Method step 110 to step 120, method step 210 to step 220 in FIG. 2, implement the functions of units 31-33 in FIG.
  • the embodiment of the present application further provides a non-transitory computer readable storage medium, the non-transient computing
  • the machine readable storage medium stores computer executable instructions that are executed by one or more processors, such as by one of the processors 401 of FIG. 4, such that the one or more processors perform any of the above
  • the object recognition method in the method embodiment for example, performs the method steps 110 to 120 in FIG. 1 described above, and the method steps 210 to 220 in FIG. 2 implement the functions of the units 31-33 in FIG.
  • the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, ie may be located A place, or it can be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • the various embodiments can be implemented by means of software plus a general hardware platform, and of course, by hardware.
  • a person skilled in the art can understand that all or part of the process of implementing the above embodiments can be completed by a computer program to instruct related hardware, and the program can be stored in a non-transitory computer readable storage medium.
  • the program when executed, may include the flow of an embodiment of the methods as described above.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).
  • the above product can perform the target recognition method provided by the embodiment of the present application, and has the corresponding functional modules and beneficial effects of performing the target recognition method.
  • the object recognition method provided by the embodiment of the present application.

Abstract

L'invention porte sur un procédé et un appareil de reconnaissance de cible ainsi que sur un terminal intelligent. Le procédé comprend les étapes qui consistent : à collecter des informations sur une cible à détecter (110), la cible à détecter comportant au moins deux types d'attributs, et une relation de priorité étant établie entre ces types d'attributs ; et à émettre, sur la base des informations, un résultat de reconnaissance de la cible à détecter (120), le résultat de reconnaissance étant un résultat de détermination qui correspond à l'un des types d'attributs, le degré de confiance du résultat de détermination remplissant une condition prédéfinie, et le type d'attributs qui correspond au résultat de reconnaissance ayant le niveau de priorité le plus élevé parmi les types d'attributs qui correspondent au résultat de détermination dont le degré de confiance remplit la condition prédéfinie. Le procédé peut garantir la fiabilité du résultat de reconnaissance émis dans différents scénarios de reconnaissance, et également émettre autant que possible des résultats de reconnaissance plus détaillés, ce qui permet d'améliorer l'expérience utilisateur.
PCT/CN2017/101967 2017-09-15 2017-09-15 Procédé et appareil de reconnaissance de cible, et terminal intelligent WO2019051814A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
PCT/CN2017/101967 WO2019051814A1 (fr) 2017-09-15 2017-09-15 Procédé et appareil de reconnaissance de cible, et terminal intelligent
CN201780002292.4A CN107995982B (zh) 2017-09-15 2017-09-15 一种目标识别方法、装置和智能终端

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/101967 WO2019051814A1 (fr) 2017-09-15 2017-09-15 Procédé et appareil de reconnaissance de cible, et terminal intelligent

Publications (1)

Publication Number Publication Date
WO2019051814A1 true WO2019051814A1 (fr) 2019-03-21

Family

ID=62039956

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/101967 WO2019051814A1 (fr) 2017-09-15 2017-09-15 Procédé et appareil de reconnaissance de cible, et terminal intelligent

Country Status (2)

Country Link
CN (1) CN107995982B (fr)
WO (1) WO2019051814A1 (fr)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110866466A (zh) * 2019-10-30 2020-03-06 平安科技（深圳）有限公司 一种人脸识别方法、装置、存储介质和服务器
CN111833298A (zh) * 2020-06-04 2020-10-27 石家庄喜高科技有限责任公司 骨骼发育等级检测方法及终端设备
CN112118278A (zh) * 2019-06-04 2020-12-22 杭州海康威视系统技术有限公司 计算节点接入方法、装置、电子设备及机器可读存储介质
CN113704266A (zh) * 2021-08-31 2021-11-26 深信服科技股份有限公司 资产信息处理方法、装置、电子设备和存储介质

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110894658B (zh) * 2018-08-23 2023-03-21 无锡小天鹅电器有限公司 洗涤控制方法、装置以及衣物处理装置
CN112071009B (zh) * 2019-06-11 2023-01-03 中国石油化工股份有限公司 光纤管道预警系统及其方法
CN112434795A (zh) * 2020-12-01 2021-03-02 中国科学院空天信息创新研究院 一种新型三维人工神经元装置及人工神经网络识别系统
CN112562221A (zh) * 2020-12-02 2021-03-26 支付宝(杭州)信息技术有限公司 一种支持人脸识别的终端以及方法
CN114627098A (zh) * 2022-03-28 2022-06-14 上海商汤智能科技有限公司 检测结果处理方法和装置、设备、介质

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070016581A1 (en) * 2005-07-13 2007-01-18 Fujitsu Limited Category setting support method and apparatus
CN101452529A (zh) * 2007-12-07 2009-06-10 索尼株式会社 信息处理设备和信息处理方法与计算机程序
CN105844283A (zh) * 2015-01-16 2016-08-10 阿里巴巴集团控股有限公司 用于识别图像类目归属的方法、图像搜索方法及装置
CN106021060A (zh) * 2016-04-29 2016-10-12 乐视控股（北京）有限公司 一种用户行为消息的优先级确定方法和装置
CN106326928A (zh) * 2016-08-24 2017-01-11 四川九洲电器集团有限责任公司 一种目标识别方法及设备
CN106934817A (zh) * 2017-02-23 2017-07-07 中国科学院自动化研究所 基于多属性的多目标跟踪方法及装置

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104143079B (zh) * 2013-05-10 2016-08-17 腾讯科技（深圳）有限公司 人脸属性识别的方法和系统
CN103914683A (zh) * 2013-12-31 2014-07-09 闻泰通讯股份有限公司 基于人脸图像的性别识别方法及系统
CN103824054B (zh) * 2014-02-17 2018-08-07 北京旷视科技有限公司 一种基于级联深度神经网络的人脸属性识别方法
US9319416B2 (en) * 2014-07-03 2016-04-19 Alcatel Lucent Priority based radius authentication
CN107145857B (zh) * 2017-04-29 2021-05-04 深圳市深网视界科技有限公司 人脸属性识别方法、装置和模型建立方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070016581A1 (en) * 2005-07-13 2007-01-18 Fujitsu Limited Category setting support method and apparatus
CN101452529A (zh) * 2007-12-07 2009-06-10 索尼株式会社 信息处理设备和信息处理方法与计算机程序
CN105844283A (zh) * 2015-01-16 2016-08-10 阿里巴巴集团控股有限公司 用于识别图像类目归属的方法、图像搜索方法及装置
CN106021060A (zh) * 2016-04-29 2016-10-12 乐视控股（北京）有限公司 一种用户行为消息的优先级确定方法和装置
CN106326928A (zh) * 2016-08-24 2017-01-11 四川九洲电器集团有限责任公司 一种目标识别方法及设备
CN106934817A (zh) * 2017-02-23 2017-07-07 中国科学院自动化研究所 基于多属性的多目标跟踪方法及装置

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112118278A (zh) * 2019-06-04 2020-12-22 杭州海康威视系统技术有限公司 计算节点接入方法、装置、电子设备及机器可读存储介质
CN112118278B (zh) * 2019-06-04 2023-07-04 杭州海康威视系统技术有限公司 计算节点接入方法、装置、电子设备及机器可读存储介质
CN110866466A (zh) * 2019-10-30 2020-03-06 平安科技（深圳）有限公司 一种人脸识别方法、装置、存储介质和服务器
CN110866466B (zh) * 2019-10-30 2023-12-26 平安科技（深圳）有限公司 一种人脸识别方法、装置、存储介质和服务器
CN111833298A (zh) * 2020-06-04 2020-10-27 石家庄喜高科技有限责任公司 骨骼发育等级检测方法及终端设备
CN111833298B (zh) * 2020-06-04 2022-08-19 石家庄喜高科技有限责任公司 骨骼发育等级检测方法及终端设备
CN113704266A (zh) * 2021-08-31 2021-11-26 深信服科技股份有限公司 资产信息处理方法、装置、电子设备和存储介质

Also Published As

Publication number Publication date
CN107995982B (zh) 2019-03-22
CN107995982A (zh) 2018-05-04

Similar Documents

Publication Publication Date Title
WO2019051814A1 (fr) Procédé et appareil de reconnaissance de cible, et terminal intelligent
US10614310B2 (en) Behavior recognition
WO2022105243A1 (fr) Procédé de détection d'événement, appareil, dispositif électronique et support de stockage
KR102570278B1 (ko) 교사 모델로부터 학생 모델을 트레이닝하는데 사용되는 학습 데이터를 생성하는 장치 및 방법
CN105122270B (zh) 使用深度传感器计数人的方法和系统
WO2019196205A1 (fr) Procédé et appareil de génération d'informations d'évaluation d'apprentissage d'une langue étrangère
WO2019056267A1 (fr) Procédé de prise de décision interactive hiérarchique, terminal interactif et serveur en nuage
JP7104779B2 (ja) 対象認識方法、装置及びインテリジェント端末
CN106648078B (zh) 应用于智能机器人的多模态交互方法及系统
WO2021103868A1 (fr) Procédé de structuration d'informations sur un piéton, dispositif, appareil et support d'enregistrement
CN115797736B (zh) 目标检测模型的训练和目标检测方法、装置、设备和介质
CN109286848B (zh) 一种终端视频信息的交互方法、装置及存储介质
KR102104548B1 (ko) 시각 감지 시스템 및 이를 이용한 시각 감지 방법
CN114093022A (zh) 活动检测装置、活动检测系统及活动检测方法
US20230245429A1 (en) Method and apparatus for training lane line detection model, electronic device and storage medium
US11194303B2 (en) Method and system for anomaly detection and notification through profiled context
CN110019837A (zh) 用户画像的生成方法及装置、计算机设备及可读介质
WO2020007191A1 (fr) Procédé et appareil de reconnaissance et de détection de corps vivant, support et dispositif électronique
CN102937412B (zh) 一种摄像设备异物遮挡检测及智能报警的方法
KR20190018274A (ko) 이미지에 포함된 특징 포인트의 시간 또는 공간의 움직임에 기초하여 이미지에 존재하는 피사체를 인식하는 장치 및 방법
CN109598712A (zh) 塑料泡沫餐盒的质量检测方法、装置、服务器及存储介质
KR101268596B1 (ko) 정사각 코드북과 다중 경계 값이 적용된 텍스쳐 특징을 이용한 전경 검출 장치 및 방법
US20230095093A1 (en) Training method and apparatus for a target detection model, target detection method and apparatus, and storage medium
WO2022089020A1 (fr) Procédé et dispositif d'affichage d'évènement, support de stockage et dispositif électronique
CN110751065B (zh) 训练数据的采集方法及装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17925019

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 17925019

Country of ref document: EP

Kind code of ref document: A1