Patent Publication Number: US-2018032793-A1

Title: Apparatus and method for recognizing objects

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority from Korean Patent Application No. 10-2016-0097836, filed on Aug. 1, 2016, the disclosure of which is incorporated herein by reference in its entirety. 
     BACKGROUND 
     1. Field 
     Apparatuses and methods consistent with example embodiments relate to recognizing an object using a plurality of object recognition apparatuses. 
     2. Discussion of Related Art 
     An object recognition system has been developed as a technique for identifying objects using a machine. A conventional object recognition system recognizes an object by comparing stored images of the object and collected images. The success rate of the object recognition system is significantly lowered when the collected images are missing or corrupted. Thus, an attempt to increase the recognition rate by collecting various images or using an algorithm for recognizing similar images has been made. However, there is a limitation in collecting images, and using the algorithm to increase the recognition rate is expensive. 
     Accordingly, there exists a need to develop a highly reliable object recognition apparatus at low cost. 
     SUMMARY 
     An objective of embodiments of the present disclosure is to provide a low-cost and highly reliable apparatus for recognizing an object. 
     According to an exemplary embodiment of the present disclosure, there is provided an apparatus for recognizing an object, including: a recognizer configured to acquire an image of a target object and recognize the target object as an object of interest by comparing the image of the target object and previously learned information about the object of interest; and a determiner configured to receive a result of recognition of the target object from at least one of other object recognition apparatuses, which performs recognition of the target object and determines whether the target object is identical to the object of interest on the basis of a result of the recognition performed by the recognizer and the received recognition result. 
     The apparatus may further include a learner configured to learn an image of the target object which is acquired by the recognizer and the at least one of other object recognition apparatuses as an information about the object of interest. 
     The recognizer may acquire an image of the target object in a different direction from that of the at least one of other object recognition apparatuses. 
     The recognizer may calculate a matching rate between the image of the target object and the object of interest and recognize the target object as the object of interest when the calculated matching rate is greater than or equal to a predetermined value, and the determiner receives a matching rate between the image of the target object and the object of interest from the at least one of other object recognition apparatuses. 
     The determiner may determine whether the target object is identical to the object of interest on the basis of a value obtained by dividing the sum of matching rates greater than or equal to the predetermined value among the calculated matching rate and the received matching rate by a total number of the apparatus and the at least one of other object recognition apparatuses. 
     When the determiner determines that the target object is identical to the object of interest and the recognizer fails to recognize the target object as the object of interest, the learner may learn the image of the target object acquired by the recognizer as an image of the object of interest. 
     When the determiner determines that the target object is identical to the object of interest and the recognizer fails to recognize the target object as the object of interest, the learner may receive an image of the target object from at least one of the at least one of other object recognition apparatuses and learn the received image as the image of the object of interest. 
     When the determiner determines that the target object is identical to the object of interest and the recognizer recognizes the target object as the object of interest, the learner may transmit the image of the target object which is acquired by the recognizer to the at least one of other object recognition apparatuses. 
     The learner may transmit a result of the learning to the at least one of other object recognition apparatuses, which is located at a position distinct from the apparatus and performs recognition of the target object. 
     According to another exemplary embodiment of the present disclosure, there is provided a method of recognizing an object which is performed by an object recognition apparatus comprising one or more processors and a memory configured to store one or more programs to be executed by the one or more processors, the method including: acquiring an image of a target object; recognizing the target object as a specific object of interest by comparing the acquired image of the target object and previously learned information about the object of interest; receiving a result of recognition of the target object from at least one of other object recognition apparatuses, which performs recognition of the target object; and determining whether the target object is identical to the object of interest on the basis of a result of the recognition of the target object and the received recognition result. 
     The method may further include, after the determining of whether the target object is identical to the object of interest, learning at least one of the acquired image and an image of the target object which is acquired by the at least one of other object recognition apparatuses as an information about the object of interest. 
     The acquiring of the image of the target object may include acquiring an image of the target object in a different direction from that of the at least one of other object recognition apparatuses. 
     The recognizing of the target object as the object of interest may include calculating a matching rate between the image of the target object and the object of interest and recognizing the target object as the object of interest when the calculated matching rate is greater than or equal to a predetermined value, wherein the result of the recognition of the target object which is received from the at least one of other object recognition apparatuses includes a matching rate between the image of the target object and the object of interest. 
     The determining of whether the target object is identical to the object of interest may include determining whether the target object is identical to the object of interest on the basis of a value obtained by dividing the sum of matching rates greater than or equal to the predetermined value among the calculated matching rate and the received matching rate by a total number of the object recognition apparatus and the at least one of other object recognition apparatuses. 
     The learning of the image of the target object may include learning the acquired image of the target object as an image of the object of interest when the target object is determined to be identical to the object of interest during the determining of whether the target object is identical to the object of interest but is not recognized as the object of interest during the recognizing of the target object. 
     The learning of the image of the target object may include receiving an image of the target object from the at least one of other object recognition apparatuses and learning the received image as the image of the object of interest when the target object is determined to be identical to the object of interest during the determining of whether the target object is identical to the object of interest but is not recognized as the object of interest during the recognizing of the target object. 
     The learning of the image of the target object may include transmitting the acquired image of the target object to the at least one of other object recognition apparatuses when the target object is determined to be identical to the object of interest during the determining of whether the target object is identical to the object of interest and is recognized as the object of interest during the recognizing of the target object. 
     The learning of the image of the target object may include transmitting a result of the learning to the at least one of other object recognition apparatuses, which is located at a position distinct from the object recognition apparatus and performs recognition of the target object. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and/or other objects, features and advantages of the present disclosure will become more apparent to those of ordinary skill in the art by describing example embodiments thereof in detail with reference to the accompanying drawings, in which: 
         FIG. 1  is an illustrative drawing for describing an operation of an object recognition apparatus according to an example embodiment; 
         FIG. 2  is a block diagram illustrating a detailed configuration of the object recognition apparatus according to an example embodiment; 
         FIG. 3  is a graph showing a recognition rate which is increased due to use of the object recognition apparatus according to an example embodiment; 
         FIG. 4  is a flowchart for describing a method of recognizing an object according to an example embodiment; and 
         FIG. 5  is a block diagram for describing a computing environment including a computing device suitable for use in an example embodiment. 
     
    
    
     DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS 
     Hereinafter, detailed example embodiments of the present disclosure will be described with reference to the accompanying drawings. The following detailed description is provided for a more comprehensive understanding of methods, devices and/or systems described in this specification. However, the methods, devices, and/or systems are only examples, and the present disclosure is not limited thereto. 
     In the description of the present disclosure, detailed descriptions of related well-known functions that are determined to unnecessarily obscure the gist of the present disclosure will be omitted. Some terms described below are defined in consideration of functions in the present disclosure, and meanings thereof may vary depending on, for example, a user or operator&#39;s intention or custom. Therefore, the meanings of terms should be interpreted based on the scope throughout this specification. The terminology used in the detailed description is provided only to describe embodiments of the present disclosure and not for purposes of limitation. Unless the context clearly indicates otherwise, the singular forms include the plural forms. It should be understood that the terms “comprises” or “includes” specify some features, numbers, steps, operations, elements, and/or combinations thereof when used herein, but do not preclude the presence or possibility of one or more other features, numbers, steps, operations, elements, and/or combinations thereof in addition to the description. 
       FIG. 1  is an illustrative drawing for describing an operation of an apparatus  100  for recognizing an object (hereinafter, referred to as an object recognition apparatus) according to an example embodiment. As shown in  FIG. 1 , the object recognition apparatus  100  according to an aspect of an example embodiment may recognize a target object  104  and determine whether the target object  104  is an object of interest to be identified on the basis of recognition results obtained from other object recognition apparatuses  102 . 
     In the example embodiments described herein, the target object  104  may be, for example, a chair, but the present disclosure is not limited thereto, and the target object  104  may be any object as long as an image of a shape thereof can be obtained by an optical device such as a camera, a camcorder, or the like. In addition, the object of interest is an object to be identified and extracted, which may be identical to the target object or different from the target object. In one example, in the case of searching for prohibited items, stolen goods, and the like among possessions of passengers at an airport, target objects may be items carried by the passengers and objects of interest may be the prohibited items, stolen goods, and the like. 
     The object recognition apparatus  100  may include an optical device, such as a camera, a camcorder, or the like, and use the optical device to recognize the target object  104 . Specifically, the object recognition apparatus  100  may recognize the target object  104  as an object of interest by comparing an obtained image of the target object  104  with previously stored information (e.g., an image, a video, or the like) related to the object of interest. Further, the object recognition apparatus  100  may receive results of recognition of the target object  104  from other object recognition apparatuses  102  and analyze the recognition results to ultimately determine whether the target object corresponds to the object of interest. Specifically, the object recognition apparatus  100  may update the previously stored information related to the object of interest on the basis of the result of the recognition of the target object  104  by the object recognition apparatus  100  and the received results of the recognition of the target object  104  by other object recognition apparatuses  102 . Thereafter, the object recognition apparatus  100  may recognize the target object using the updated information related to the object of interest. 
     Each of other object recognition apparatuses  102  may be an apparatus which recognizes the target object  104 . According to an aspect of an example embodiment, other object recognition apparatuses  102  may acquire an image of the target object  104  in a different direction from that of the object recognition apparatus  100 . In this case, the image of the target object  104  may vary depending on an angle at which the target object  104  is viewed. Therefore, the object recognition apparatus  100  and other object recognition apparatuses  102  may obtain different recognition results even for the same target object. 
     However, each of other object recognition apparatuses  102  may be an apparatus configured to be the same as the object recognition apparatus  100  according to an example embodiment, but is not limited thereto, and may be an apparatus which simply recognizes only the target object. That is, other object recognition apparatuses  102  may not include a learner  206  which will be described below. 
       FIG. 2  is a block diagram illustrating a detailed configuration of the object recognition apparatus  100  according to an example embodiment. As shown in  FIG. 2 , the object recognition apparatus  100  may include a recognizer  202 , a determiner  204 , and the learner  206 . Each of the components and modules of the object recognition apparatus  100  as shown in  FIG. 100  and other figures may be implemented with hardware (e.g., a processor, a computer-readable storage medium, etc.), software (e.g. a computer program instructions), or a combination of both. 
     The recognizer  202  is a module which recognizes the target object  104 . Specifically, the recognizer  202  may acquire an image of the target object  104  and recognize the target object  104  on the basis of the acquired image. The recognizer  202  may recognize the target object  104  as an object of interest. In other words, the recognizer  202  may autonomously determine whether the target object  104  is the object of interest. 
     To this end, the recognizer  202  may include an optical device such as a camera, a camcorder, or the like. According to an aspect of an example embodiment, the recognizer  202  may acquire the image of the target object  104  by photographing the target object  104  using the optical device. In addition, the recognizer  202  may acquire the image of the target object  104  in a different direction (e.g., different viewing angle) from that of other object recognition apparatuses  102 . In other words, the object recognition apparatus  100  and other object recognition apparatuses  102  according to an example embodiment may acquire images of the target object  104  from different angles. 
     The recognizer  202  may compare the acquired image of the target object  104  with previously learned information about the object of interest. The previously learned information about the object of interest may be information to be considered for determining whether the target object  104  is the object of interest, and may include, for example, a group of images of the object of interest. The recognizer  202  may compare the image of the target object and the object of interest using a conventional object recognition algorithm. According to an aspect of an example embodiment, the recognizer  202  may calculate a matching rate between the image of the target object  104  and the object of interest and may recognize the target object  104  as the object of interest when the calculated matching rate is greater than or equal to a predetermined value (e.g., 0.75 or 0.8). Conversely, when the calculated matching rate is less than or equal to the predetermined value, the recognizer  202  may recognize the target object  104  as an object of non-interest. A matching rate may be an objective measurement of how closely the image of the target object  104  resembles the object of interest, where the matching rate of 0 represents no resemblance and the matching rate of 1 represents a complete match. 
     The determiner  204  is a module which ultimately determines whether the target object  104  is an object of interest by considering the results of the recognition of the target object  104  by other object recognition apparatuses  102  in addition to the result of the recognition performed by the object recognition apparatus  100 . 
     The determiner  204  may receive the recognition results of the target object  104  from other object recognition apparatuses  102 . The determiner  204  may receive information about the matching rate between the target object  104  and the object of interest from other object recognition apparatuses  102 . In other words, the received result of the recognition of the target object  104  by other object recognition apparatuses  102  may include information about whether the target object  104  corresponds to the object of interest as well as information about the matching rate between the target object  104  and the object of interest. 
     The determiner  204  may determine whether the target object  104  corresponds to the object of interest on the basis of the result of recognition autonomously performed by the object recognition apparatus  100 , that is, the recognizer  202 , in addition to the recognition result received from other object recognition apparatuses  102 . 
     According to an aspect of an example embodiment, when any matching rate among the matching rate calculated by the recognizer  202  and the matching rates received from other object recognition apparatuses  102  are greater than or equal to a predetermined value (e.g., 0.75 or 0.8), the sum of the matching rates may be divided by the total number of the object recognition apparatus  100  and other object recognition apparatuses  102 , and on the basis of the resulting value (hereinafter, referred to as an “allowable matching rate”), the determiner  204  may determine whether the target object  104  corresponds to the object of interest. Specifically, the determiner  204  may determine that the target object  104  corresponds to the object of interest when the allowable matching rate is greater than or equal to the predetermined threshold value (e.g., 0.75, 0.8, or the like). 
     For example, it is assumed that the object recognition apparatus  100  and four other object recognition apparatuses  102 - 1 ,  102 - 2 ,  102 - 3 , and  102 - 4  recognize the target object. In this case, it is also assumed that matching rates between the target object and the object of interest, which are obtained from the total of five apparatuses, are 0.9834, 0.8843, 0.9654, 0.9492, and 0.3213, and when the matching rate obtained by each of the object recognition apparatuses  100  and  102  is greater than or equal to a threshold value (e.g., 0.75), the corresponding object recognition apparatuses autonomously determines that the target object corresponds to the object of interest. In this example, the allowable matching rate may be a value obtained by dividing the sum of the matching rates (e.g., 0.9834, 0.8843, 0.9654, and 0.9492) obtained from the object recognition apparatuses  100  and/or  102  which determine that the target object corresponds to the object of interest by the object recognition apparatuses  100  and  102 . In this case, the allowable matching rate is 0.75646, i.e., (0.9834+0.8843+0.9654+0.9492)/5. In the case in which the determiner  204  ultimately determines that the target object corresponds to the object of interest when the allowable matching rate is greater than or equal to the threshold value (e.g., 0.75), the determiner  204  in the above example may determine that the target object corresponds to the object of interest. 
     According to an aspect of an example embodiment, whether the target object is identical to the object of interest is determined by considering the result of the recognition of the target object  104  by the recognizer  202  of the object recognition apparatus  100  as well as the recognition results received from other object recognition apparatuses  102  so that reliability of the recognition result of the target object  104  may be increased. 
     Meanwhile, although the recognizer  202  and the determiner  204  are shown separately in  FIG. 1  for illustrative purposes, it should be appreciated that the recognizer  202  and the determiner  204  may be integrated into a single configuration according to some example embodiments. 
     The learner  206  is a module for learning information about the object of interest. According to an aspect of an example embodiment, the learner  206  may learn an acquired image of the target object  104  as the information about the object of interest according to the determination result obtained from the determiner  204 . In other words, the learner  206  may learn images of the target object  104  acquired from the recognizer  202  and at least one of other object recognition apparatuses  102  as the information about the object of interest. Specifically, the learner  206  may pre-store (e.g., store before the recognizer  202  acquires images of the target object  104 ) the information about the object of interest. In this case, the information about the object of interest may be a group of images corresponding to the object of interest. In addition, the object recognition apparatus  100  may include a database for storing the information about the object of interest. Then, the learner may update the information about the object of interest using image of the target object  104  acquired by the recognizer or received from any of other object recognition apparatuses  102 . In other words, the learner  206  may store the acquired or received images as images of the object of interest which are viewed from different directions and different angles. Accordingly, the learner  206  may collect various images according to the positions and angles at which the object of interest is photographed, and the recognizer  202  may accurately recognize the object of interest using the collected images. Hereinafter, a process of the learner  206  learning the image of the target object  104  will be described in detail. 
     According to an aspect of an example embodiment, when the determiner  204  determines that the target object  104  corresponds to the object of interest and the recognizer  202  fails to recognize the target object  104  as the object of interest, the learner  206  may learn (e.g., through machine learning, without the knowledge that the target object  104  is an object of interest being explicitly programmed) an image of the target object  104  acquired by the recognizer  202  as an image of the object of interest. Specifically, when the recognizer  202  fails to recognize the target object  104  as the object of interest while the determiner  204  determines that the target object  104  corresponds to the object of interest, the learner  206  may learn the image acquired by the recognizer  202  as the image of the object of interest. In addition, in an example embodiment, the learner  206  may receive an image of the target object  104  acquired by other object recognition apparatus  102  and learn the received image of the target object  104  as the image of the object of interest. In this case, the received image of the target object  104  may be an image of the target object  104  that is photographed at a different angle from the image of the target object  104  acquired by the recognizer  202 . 
     When the determiner  204  determines that the image of the target object  104  acquired by the recognizer  202  is the image of the object of interest, the learner  206  may transmit the acquired image to other object recognition apparatuses  102 . According to an aspect of an example embodiment, when the determiner  204  determines that the target object  104  corresponds to the object of interest and the recognizer  202  recognizes the target object  104  as the object of interest, the learner  206  may transmit the image of the target object  104  acquired by the recognizer  202  to other object recognition apparatuses  102 . According to an aspect of an example embodiment, the learner  206  may transmit the image acquired by the recognizer  202  to other object recognition apparatuses  102  only when the determiner  204  determines that the target object  104  corresponds to the object of interest and the recognizer  202  recognizes the target object  104  as the object of interest. However, the present disclosure is not limited thereto, and the learner  206  may transmit the acquired image to other object recognition apparatuses  102  regardless of the determination result. In this case, the learner  206  may selectively learn the images received from other object recognition apparatuses  102 . It is enough for the object recognition apparatus  100  to be able to share the images with other object recognition apparatuses  102 , and a manner of sharing the images is not particularly limited. According to example embodiments of the present disclosure, it is possible to easily collect images according to a position and angle at which the object of interest is photographed by sharing the images acquired by the object recognition apparatus and other object recognition apparatuses. 
     The learner  206  may transmit the learning result to another object recognition apparatus, which is located at a position distinct from the object recognition apparatus  100  and recognizes the target object  104 . In this case, the distinct position may refer to a position which is distant enough (e.g., above a threshold value) from the object recognition apparatus  100  so that the target object, which is located in one direction therefrom, is not photographed by an optical device provided in another direction. In addition, the learning result may be an image related to the object of interest and may include the acquired image and the received images. In other words, the learner  206  may transmit the acquired image and the received images to another object recognition apparatus that has not yet acquired an image of the target object  104 . In this case, the learner  206  may transmit information on a corresponding object of interest (e.g., a name of the object of interest, identification information thereof, etc.), with the acquired image and the received images. Accordingly, each of other object recognition apparatuses may be allowed to immediately recognize the target object using the received images without needing to learn the object of interest or determine whether the target object corresponds to the object of interest. 
     According to example embodiments of the present disclosure, since the object recognition apparatuses share recognition results and learn the information about the target object through the shared recognition results, it is possible to improve accuracy of the recognition rate of the target object. In addition, it is possible to improve the recognition rate of the object recognition apparatus  100  at low cost by utilizing existing optical devices such as a camera, a camcorder, or the like. 
     However, the recognizer  202 , the determiner  204 , and the learner  206  are only distinguished functionally, and each configuration is not necessarily implemented as a separate hardware component. In other words, two or more of the recognizer  202 , the determiner  204 , and the learner  206  may be implemented as a single piece of hardware (e.g., a processor), a software module (e.g., instructions stored in a computer-readable storage medium), or a combination of both. Alternatively, the recognizer  202 , the determiner  204 , and the learner  206  may be each implemented with its own hardware module, a software module, or a combination of both. 
       FIG. 3  is a graph showing a recognition rate which is increased due to the use of the object recognition apparatus  100  according to an example embodiment.  FIG. 3  shows a result of a simulation performed under the assumption that the object recognition rate of the object recognition apparatus  100  is 50% (or 0.5). The object recognition rate may refer to reliability of the result of recognition of the target object  104  by the object recognition apparatus  100 . For example, a high object recognition rate indicates that the object recognition apparatus  100  accurately recognizes the target object  104  as the object of interest. Thus, the match rate may indicate a determination, by a machine, of how much an image of an object resembles an object of interest, while the object recognition rate may indicate the probability of the machine actually correctly recognizing the object in the image to be the object of interest. 
     As shown in  FIG. 3 , as the number of object recognition apparatuses increases, an object recognition rate by the plurality of object recognition apparatuses may converge to the object recognition rate of the object recognition apparatus  100  (e.g., 0.5). 
     Then, after the object recognition apparatus  100  performs learning once, an object recognition rate {circle around (1)} for the plurality of object recognition apparatuses may increase above 0.5. Then, after the object recognition apparatus  100  performs the learning one more time, an object recognition rate {circle around (2)} for the plurality of object recognition apparatuses may further increase. 
       FIG. 4  is a flowchart for describing a method  400  of recognizing an object according to an example embodiment. The method shown in  FIG. 4  may be performed by the above-described object recognition apparatus  100 . Although the method shown in the flowchart is divided into a plurality of operations, the operations may be combined and concurrently performed, some operations may be omitted or further divided into more operations, or any operation that is not shown in the flowchart may be added and performed. 
     The recognizer  202  may acquire an image of a target object (S 402 ). The target object  104  may be, for example, a chair as an object to be identified, but the present disclosure is not limited thereto, and the target object  104  may include any object as long as an image of a shape thereof can be obtained by an optical device such as a camera, a camcorder, or the like. According to an aspect of an example embodiment, the recognizer  202  may acquire the image of the target object  104  in a different direction (e.g., from a different viewing angle) from that of at least one of other object recognition apparatuses  102 . Accordingly, the object recognition apparatus  100  and other object recognition apparatuses  102  may acquire images according to a respective angle at which the target object is viewed. 
     Then, the recognizer  202  may recognize the target object  104  as an object of interest by comparing the acquired image of the target object  104  and previously learned information about the object of interest (e.g., an image or a video of the object of interest) (S 404 ). The object of interest may be an object to be recognized and extracted, and may be identical to or different from the target object  104 . The recognizer  202  may calculate a matching rate between the image of the target object  104  and the object of interest and recognize the target object  104  as the object of interest when the matching rate is greater than or equal to a predetermined value. 
     Then, the determiner  204  may receive a result of recognition of the target object  104  from at least one of the other object recognition apparatuses  102 , which recognize the target object  104  (S 406 ). In this case, the recognition result of the target object  104  may include the matching rate (e.g., 0.8843, 0.9654, etc.) between the image of the target object  104  and the object of interest. 
     Then, the determiner  204  may determine whether the target object  104  corresponds to the object of interest on the basis of the recognition result of the target object  104  and the received recognition result (S 408 ). Specifically, the determiner  204  may determine whether the target object  104  corresponds to the object of interest on the basis of a value obtained by dividing the sum of matching rates that are greater than or equal to the predetermined value among the calculated matching rate and the received matching rates by the total number of the object recognition apparatus  100  and the at least one of other object recognition apparatuses  102 . 
     Then, when the determiner  204  determines that the target object  104  is corresponds the object of interest, the learner  206  may learn images of the target object  104  acquired from the recognizer  202  and at least one of other object recognition apparatuses  102  as the information about the object of interest (S 410 ). When the determiner  204  determines that the target object  104  corresponds to the object of interest and the recognizer  202  fails to recognize the target object  104  as the object of interest, the learner  206  may learn the image of the target object  104  acquired by the recognizer  202  as an image of the object of interest. In this case, the learner  206  may receive an image of the target object  104  from at least one of other object recognition apparatuses  102  and learn the image as the image of the object of interest. In addition, when the determiner  204  determines that the target object  104  corresponds to the object of interest and the recognizer  202  recognizes the target object  104  as the object of interest, the learner  206  may transmit the image of the target object  104  acquired by the recognizer  202  to at least one of the other object recognition apparatuses  102 . In this case, other object recognition apparatuses  102  may learn the image transmitted from the object recognition apparatus  100 . Meanwhile, the learner  206  may transmit the learning result to at least one of other object recognition apparatuses, which is located at a position distinct from the object recognition apparatus  100  and recognize the target object  104 . Here, the learning result may be a group of images related to the target object  104 . Accordingly, other object recognition apparatuses that have not yet acquired the image of the target object  104  may accurately recognize the object of interest through the learning result only. 
     Meanwhile, according to an aspect of an example embodiment, when the determiner  204  determines that the target object  104  does not correspond to the object of interest, the recognizer  202  may acquire a new image of the target object  104  and perform recognition of the target object  104 . 
       FIG. 5  is a block diagram for describing a computing environment  10  including a computing device suitable for use in an example embodiment. That is,  FIG. 5  is a diagram for describing a hardware aspect for implementing an example embodiment. Each component may have a different function or capability other than those described hereinafter, and, in addition to components that will be described hereinafter, other components may be further included. 
     The illustrated computing environment  10  includes a computing device  12 . In an example embodiment, the computing device  12  may be the object recognition apparatus  100 . In addition, the computing device  12  may be each of the other object recognition apparatuses  102 . 
     The computing device  12  may include at least one processor  14 , a computer-readable storage medium  16 , and a communication bus  18 . The processor  14  may operate according to one or more of the above-described example embodiments. For example, the processor  14  may execute one or more programs  20  stored in the computer-readable storage medium  16 . The one or more programs may include one or more computer-executable instructions, and when the computer-executable instructions are executed by the processor  14 , the computing device  12  may perform the operations according to an example embodiment. The processor  14  may be, for example, a central processing unit (CPU), an application processor (AP), a system on a chip (SoC), an application-specific integrated circuit (ASIC), etc. 
     The computer-readable storage medium  16  may be configured to store computer-executable instructions, program code, program data, and/or other suitable forms of information. The programs  20  stored in the computer-readable storage medium  16  may include a group of instructions executable by the processor  14 . The computer-readable storage medium  16  may include a memory (a volatile memory such as a random access memory (RAM), a non-volatile memory, or a suitable combination thereof), one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, other forms of storage media accessible by the computing device  12  and capable of storing desired information, or any suitable combination thereof. 
     The communication bus  18  interconnects various components of the computing device  12  including the processor  14  and the computer-readable storage medium  16 . 
     The computing device  12  may include one or more network communication interfaces  26  and one or more input/output interfaces  22  for one or more input/output devices  24 . The input/output interface  22  and the network communication interface  26  are connected to the communication bus  18 . The input/output device  24  may be connected to other components of the computing device  12  through the input/output interface  22 . The illustrative input/output device  24  may include a pointing device (e.g., a mouse or a track pad), a keyboard, a touch input device (e.g., a touch pad or a touch screen), a voice or sound input device, input devices such as various types of sensor devices and/or a photographing device, and/or output devices such as a display device, a printer, a speaker, and/or a network card. The illustrative input/output device  24  may be included within the computing device  12  as one component included in the computing device  12  or may be connected to another computing device  102  as a separate device distinct from the computing device  12 . The network communication interface  26  may be, for example, a modem, a network interface controller (NIC), a network adapter, an antenna, etc. 
     According to example embodiments of the present disclosure, since whether a target object is identical to an object of interest is determined by comprehensively considering a result of recognition of the target object by an object recognition apparatus and a recognition result received from another object recognition apparatus, it is possible to improve reliability of the recognition result of the target object. 
     In addition, according to the example embodiments of the present disclosure, it is possible to easily collect images of each angle of the object of interest by sharing images of the object of interest acquired by object recognition apparatus and other object recognition apparatuses respectively. 
     Moreover, according to the example embodiments of the present disclosure, since the object recognition apparatuses share recognition results of the target object and learn information about the target object through the shared recognition results, it is possible to improve accuracy of the recognition rate of the target object. 
     Furthermore, according to the example embodiments of the present disclosure, an object recognition apparatus which has not actually photographed the target object may easily recognize the object of interest by receiving learning results of other object recognition apparatuses being shared therewith. 
     Although example embodiments of the present disclosure have been described in detail, it should be understood by those skilled in the art that various changes may be made without departing from the spirit or scope of the present disclosure. Therefore, the scope of the present disclosure is to be determined by the following claims and their equivalents, and is not restricted or limited by the foregoing detailed description.