Patent Publication Number: US-10769794-B2

Title: Multi-sensor object recognition system and method

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 15/698,288, entitled “Multi-Sensor Object Recognition System and Method,” filed Sep. 7, 2017, which is incorporated herein by reference in its entirety. 
    
    
     BACKGROUND 
     Environments in which inventories of objects are managed, such as products for purchase in a retail facility, may be complex and fluid. For example, a given environment may contain a wide variety of objects with different attributes (size, shape, color and the like). Further, the placement and quantity of the objects in the environment may change frequently. Still further, imaging conditions such as lighting may be variable both over time and at different locations in the environment. These factors may reduce the accuracy with which information concerning the objects may be derived from image data captured within the environment. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       The accompanying figures, where like reference numerals refer to identical or functionally similar elements throughout the separate views, together with the detailed description below, are incorporated in and form part of the specification, and serve to further illustrate embodiments of concepts that include the claimed invention, and explain various principles and advantages of those embodiments. 
         FIG. 1  is a schematic of a mobile automation system. 
         FIG. 2A  depicts a mobile automation apparatus in the system of  FIG. 1 . 
         FIG. 2B  is a block diagram of certain internal hardware components of the mobile automation apparatus in the system of  FIG. 1 . 
         FIG. 2C  is a block diagram of certain internal hardware components of the server in the system of  FIG. 1 . 
         FIG. 3  is a flowchart of a method of support surface edge detection. 
         FIGS. 4A-4B  depict example images and input object indicators obtained in the performance of the method of  FIG. 3 . 
         FIG. 5  depicts a method of performing block  310  of the method of  FIG. 3 . 
         FIGS. 6A-6B  depict an example of the performance of the method of  FIG. 5 . 
         FIGS. 7A-7C  depict a method of performing block  315  of the method of  FIG. 3  and associated input data. 
         FIG. 8  depicts a method of performing block  320  of the method of  FIG. 3   
         FIGS. 9A and 9B  depict example results of the performance of the method of  FIG. 3 . 
     
    
    
     Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of embodiments of the present invention. 
     The apparatus and method components have been represented where appropriate by conventional symbols in the drawings, showing only those specific details that are pertinent to understanding the embodiments of the present invention so as not to obscure the disclosure with details that will be readily apparent to those of ordinary skill in the art having the benefit of the description herein. 
     DETAILED DESCRIPTION 
     Implementing autonomous or semi-autonomous inventory management in certain environments, such as retail facilities, warehouses and the like, presents various challenges, among which is the scale of the facility and the structures upon which the objects (e.g. products for purchase in a retail facility) are disposed. For example, a mobile apparatus may be configured to travel the facility and capture images of the objects for downstream processing to identify the objects and derive status information corresponding to the objects. Such a mobile apparatus may be equipped with an image sensor, such as a digital camera. However, an image sensor and corresponding optics to enable the apparatus to capture an image of, for example, the entire height of a shelf module in a retail facility, may be costly or simply unavailable. 
     The mobile apparatus may instead carry a set of image sensors disposed to capture adjacent regions, such as portions of the above-mentioned shelf module. Although this approach may mitigate the challenges associated with providing appropriate image sensing equipment, the resulting plurality of images may depict the same regions of the facility in multiple images. Further, some objects may not be fully depicted in any given image, instead being partially depicted in two or more images. The partial depiction of objects between images, and the duplicated depiction of objects in images, may reduce the accuracy with which objects may be identified, as well as the accuracy with which status information may be derived from object identification results. 
     Examples disclosed herein are directed to a method of object detection in an imaging controller. The method includes obtaining a set of images depicting overlapping regions of an area containing a plurality of objects. Each of the set of images includes a plurality of input object indicators defined by respective (i) input bounding boxes, (ii) input confidence level values, and (iii) object identifiers. The method further includes identifying candidate subsets of input object indicators in adjacent ones of the set of images. Each candidate subset has input bounding boxes that overlap in a common frame of reference, and a common object identifier. The method further includes adjusting the input confidence level values upwards for each input object indicator in the candidate subsets; selecting clusters of the input object indicators, the input object indicators of each cluster satisfying a minimum input confidence threshold, having a common object identifier, and having a degree of overlap that satisfies a predefined threshold; and detecting an object by generating a single output object indicator for each cluster, the output object indicator having (i) an output bounding box, (ii) an output confidence level value, and (iii) the common object identifier. 
     Further examples disclosed herein are directed to a computing device for detecting objects, the computing device comprising: a memory; an imaging controller comprising: an image preprocessor configured to obtain a set of images from the memory depicting overlapping regions of an area containing a plurality of objects; each of the set of images including a plurality of input object indicators defined by respective (i) input bounding boxes, (ii) input confidence level values, and (iii) object identifiers; a subset detector configured to identify candidate subsets of input object indicators in adjacent ones of the set of images, each candidate subset having input bounding boxes that overlap in a common frame of reference, and a common object identifier; the subset detector further configured to adjust the input confidence level values upwards for each input object indicator in the candidate subsets; a cluster detector configured to select clusters of the input object indicators, the input object indicators of each cluster satisfying a minimum input confidence threshold, having a common object identifier, and having a degree of overlap that satisfies a predefined threshold; and an output generator configured to detect an object by generating a single output object indicator for each cluster, the output object indicator having (i) an output bounding box, (ii) an output confidence level value, and (iii) the common object identifier. 
     Still further examples disclosed herein are directed to a non-transitory computer readable storage medium containing a plurality of computer readable instructions executable by an imaging controller to configure the imaging controller to perform a method of object detection comprising: obtaining a set of images depicting overlapping regions of an area containing a plurality of objects; each of the set of images including a plurality of input object indicators defined by respective (i) input bounding boxes, (ii) input confidence level values, and (iii) object identifiers; identifying candidate subsets of input object indicators in adjacent ones of the set of images, each candidate subset having input bounding boxes that overlap in a common frame of reference, and a common object identifier; adjusting the input confidence level values upwards for each input object indicator in the candidate subsets; selecting clusters of the input object indicators, the input object indicators of each cluster satisfying a minimum input confidence threshold, having a common object identifier, and having a degree of overlap that satisfies a predefined threshold; and detecting an object by generating a single output object indicator for each cluster, the output object indicator having (i) an output bounding box, (ii) an output confidence level value, and (iii) the common object identifier. 
       FIG. 1  depicts a mobile automation system  100  in accordance with the teachings of this disclosure. The system  100  includes a server  101  in communication with at least one mobile automation apparatus  103  (also referred to herein simply as the apparatus  103 ) and at least one client computing device  105  via communication links  107 , illustrated in the present example as including wireless links. In the present example, the links  107  are provided by a wireless local area network (WLAN) deployed within the retail environment by one or more access points. In other examples, the server  101 , the client device  105 , or both, are located outside the retail environment, and the links  107  therefore include wide-area networks such as the Internet, mobile networks, and the like. As will be described in greater detail below, the system  100  also includes a dock  108  for the apparatus  103 . The dock  108  is in communication with the server  101  via a link  109  that in the present example is a wired link. In other examples, however, the link  109  is a wireless link. 
     The client computing device  105  is illustrated in  FIG. 1  as a mobile computing device, such as a tablet, smart phone or the like. In other examples, the client device  105  includes computing devices such as a desktop computer, a laptop computer, another server, a kiosk, a monitor, or other suitable device. The system  100  can include a plurality of client devices  105 , each in communication with the server  101  via respective links  107 . 
     The system  100  is deployed, in the illustrated example, in a retail environment including a plurality of shelf modules  110 - 1 ,  110 - 2 ,  110 - 3  and so on (collectively referred to as shelves  110 , and generically referred to as a shelf  110 —this nomenclature is also employed for other elements discussed herein). Each shelf module  110  supports a plurality of products  112 . Each shelf module  110  includes a shelf back  116 - 1 ,  116 - 2 ,  116 - 3  and a support surface (e.g. support surface  117 - 3  as illustrated in  FIG. 1 ) extending from the shelf back  116  to a shelf edge  118 - 1 ,  118 - 2 ,  118 - 3 . The shelf modules  110  are typically arranged in a plurality of aisles, each of which includes a plurality of modules aligned end-to-end. In such arrangements, the shelf edges  118  face into the aisles, through which customers in the retail environment as well as the apparatus  103  may travel. As will be apparent from  FIG. 1 , the term “shelf edge”  118  as employed herein, which may also be referred to as the edge of a support surface (e.g., the support surfaces  117 ) refers to a surface bounded by adjacent surfaces having different angles of inclination. In the example illustrated in  FIG. 1 , the shelf edge  118 - 3  is at an angle of about ninety degrees relative to each of the support surface  117 - 3  and the underside (not shown) of the support surface  117 - 3 . In other examples, the angles between the shelf edge  118 - 3  and the adjacent surfaces, such as the support surface  117 - 3 , is more or less than ninety degrees. 
     More specifically, the apparatus  103  is deployed within the retail environment, and communicates with the server  101  (via the link  107 ) to navigate, autonomously or partially autonomously, the length  119  of at least a portion of the shelves  110 . The apparatus  103  is equipped with a plurality of navigation and data capture sensors  104 , such as image sensors (e.g. one or more digital cameras) and depth sensors (e.g. one or more Light Detection and Ranging (LIDAR) sensors, one or more depth cameras employing structured light patterns, such as infrared light), and is further configured to employ the sensors to capture shelf data. In the present example, the apparatus  103  is configured to capture, at each of a plurality of positions along the length  119  of a shelf  110 , a set of images depicting the shelf  110 . As will be described below in greater detail, the apparatus  103  is configured such that each set of images depicts overlapping regions of an area of the shelf  110 . As the apparatus  103  moves along the length  119 , another set of images is captured, depicting overlapping regions of an adjacent area. In other words, in the present example, the apparatus  103  is configured, e.g. via cameras with fields of view spaced apart vertically, to capture sets of images depicting adjacent substantially vertical segments of each shelf  110 . 
     The server  101  includes a special purpose imaging controller, such as a processor  120 , specifically designed to control the mobile automation apparatus  103  to capture data (e.g. the above-mentioned image sets), obtain the captured data via a communications interface  124  and store the captured data in a repository  132  in a memory  122 . The server  101  is further configured to perform various post-processing operations on the captured data to obtain and refine object identifications from the captured data. The post-processing of captured data by the server  101  will be discussed below in greater detail. The server  101  may also be configured to determine product status data based in part on the above-mentioned product identifications, and to transmit status notifications (e.g. notifications indicating that products are out-of-stock, low stock or misplaced) to the mobile device  105  responsive to the determination of product status data. 
     The processor  120  is interconnected with a non-transitory computer readable storage medium, such as the above-mentioned memory  122 , having stored thereon computer readable instructions for executing control of the apparatus  103  to capture data, as well as the above-mentioned post-processing functionality, as discussed in further detail below. The memory  122  includes a combination of volatile (e.g. Random Access Memory or RAM) and non-volatile memory (e.g. read only memory or ROM, Electrically Erasable Programmable Read Only Memory or EEPROM, flash memory). The processor  120  and the memory  122  each comprise one or more integrated circuits. In an embodiment, the processor  120 , further includes one or more central processing units (CPUs) and/or graphics processing units (GPUs). In an embodiment, a specially designed integrated circuit, such as a Field Programmable Gate Array (FPGA), is designed to perform the object identification refinement discussed herein, either alternatively or in addition to the imaging controller/processor  120  and memory  122 . As those of skill in the art will realize, the mobile automation apparatus  103  also includes one or more controllers or processors and/or FPGAs, in communication with the controller  120 , specifically configured to control navigational and/or data capture aspects of the apparatus  103 . The client device  105  also includes one or more controllers or processors and/or FPGAs, in communication with the controller  120 , specifically configured to process (e.g. to display) notifications received from the server  101 . 
     The server  101  also includes the above-mentioned communications interface  124  interconnected with the processor  120 . The communications interface  124  includes suitable hardware (e.g. transmitters, receivers, network interface controllers and the like) allowing the server  101  to communicate with other computing devices—particularly the apparatus  103 , the client device  105  and the dock  108 —via the links  107  and  109 . The links  107  and  109  may be direct links, or links that traverse one or more networks, including both local and wide-area networks. The specific components of the communications interface  124  are selected based on the type of network or other links that the server  101  is required to communicate over. In the present example, as noted earlier, a wireless local-area network is implemented within the retail environment via the deployment of one or more wireless access points. The links  107  therefore include either or both wireless links between the apparatus  103  and the mobile device  105  and the above-mentioned access points, and a wired link (e.g. an Ethernet-based link) between the server  101  and the access point. 
     The memory  122  stores a plurality of applications, each including a plurality of computer readable instructions executable by the processor  120 . The execution of the above-mentioned instructions by the processor  120  configures the server  101  to perform various actions discussed herein. The applications stored in the memory  122  include a control application  128 , which may also be implemented as a suite of logically distinct applications. In general, via execution of the control application  128  or subcomponents thereof, the processor  120  is configured to implement various functionality. The processor  120 , as configured via the execution of the control application  128 , is also referred to herein as the controller  120 . As will now be apparent, some or all of the functionality implemented by the controller  120  described below may also be performed by preconfigured hardware elements (e.g. one or more Application-Specific Integrated Circuits (ASICs)) rather than by execution of the control application  128  by the processor  120 . 
     Turning now to  FIGS. 2A and 2B , the mobile automation apparatus  103  is shown in greater detail. The apparatus  103  includes a chassis  201  containing a locomotive mechanism  203  (e.g. one or more electrical motors driving wheels, tracks or the like). The apparatus  103  further includes a sensor mast  205  supported on the chassis  201  and, in the present example, extending upwards (e.g., substantially vertically) from the chassis  201 . The mast  205  supports the sensors  104  mentioned earlier. In particular, the sensors  104  include at least one imaging sensor  207 , such as a digital camera, as well as at least one depth-sensing sensor  209 , such as a  3 D digital camera. The apparatus  103  also includes additional depth sensors, such as LIDAR sensors  211 . In other examples, the apparatus  103  includes additional sensors, such as one or more RFID readers, temperature sensors, and the like. 
     In the present example, the mast  205  supports seven digital cameras  207 - 1  through  207 - 7 , and two LIDAR sensors  211 - 1  and  211 - 2 . The mast  205  also supports a plurality of illumination assemblies  213 , configured to illuminate the fields of view of the respective cameras  207 . That is, the illumination assembly  213 - 1  illuminates the field of view of the camera  207 - 1 , and so on. The sensors  207  and  211  are oriented on the mast  205  such that the fields of view of each sensor face a shelf  110  along the length  119  of which the apparatus  103  is travelling. The apparatus  103  is configured to track a location of the apparatus  103  (e.g. a location of the center of the chassis  201 ) in a common frame of reference previously established in the retail facility, permitting data captured by the mobile automation apparatus  103  to be registered to the common frame of reference. 
     To that end, the mobile automation apparatus  103  includes a special-purpose controller, such as a processor  220 , as shown in  FIG. 2B , interconnected with a non-transitory computer readable storage medium, such as a memory  222 . The memory  222  includes a combination of volatile (e.g. Random Access Memory or RAM) and non-volatile memory (e.g. read only memory or ROM, Electrically Erasable Programmable Read Only Memory or EEPROM, flash memory). The processor  220  and the memory  222  each comprise one or more integrated circuits. The memory  222  stores computer readable instructions for execution by the processor  220 . In particular, the memory  222  stores a control application  228  which, when executed by the processor  220 , configures the processor  220  to perform various functions related to the navigation of the apparatus  103  (e.g. by controlling the locomotive mechanism  203 ) and to the detection of objects in data captured by the sensors (e.g. the cameras  207 ). The application  228  may also be implemented as a suite of distinct applications in other examples. 
     The processor  220 , when so configured by the execution of the application  228 , may also be referred to as a controller  220  or, in the context of object detection from captured data, as an imaging controller  220 . Those skilled in the art will appreciate that the functionality implemented by the processor  220  via the execution of the application  228  may also be implemented by one or more specially designed hardware and firmware components, such as FPGAs, ASICs and the like in other embodiments. 
     The memory  222  may also store a repository  232  containing, for example, a map of the environment in which the apparatus  103  operates, for use during the execution of the application  228 . The apparatus  103  may communicate with the server  101 , for example to receive instructions to initiate data capture operations, via a communications interface  224  over the link  107  shown in  FIG. 1 . The communications interface  224  also enables the apparatus  103  to communicate with the server  101  via the dock  108  and the link  109 . 
     In the present example, as discussed below, one or both of the server  101  (as configured via the execution of the control application  128  by the processor  120 ) and the mobile automation apparatus  103  (as configured via the execution of the application  228  by the processor  220 ), are configured to process images captured by the apparatus  103  to obtain and refine object identifications therefrom. In further examples, the data processing discussed below may be performed on a computing device other than the server  101  and the mobile automation apparatus  103 , such as the client device  105 . The data processing mentioned above will be described in greater detail in connection with its performance at the server  101 , via execution of the application  128 . 
     Turning now to  FIG. 2C , before describing the operation of the application  128  to obtain and refine object identifications, certain components of the application  128  will be described in greater detail. As will be apparent to those skilled in the art, in other examples the components of the application  128  may be separated into distinct applications, or combined into other sets of components. Some or all of the components illustrated in  FIG. 2C  may also be implemented as dedicated hardware components, such as one or more ASICs or FPGAs. For example, in one embodiment, to improve reliability and processing speed, at least some of the components of  FIG. 2C  are programmed directly into the imaging controller  120 , which may be an FPGA or an ASIC having circuit and memory configuration specifically designed to optimize image processing of a high volume of sensor data received from the mobile automation apparatus  103 . In such an embodiment, some or all of the control application  128 , discussed below, is an FPGA or an ASIC chip. 
     The control application  128  includes an image preprocessor  200  configured to obtain sets of images depicting the shelves  110  and the products  112  supported thereon, and to obtain (e.g. embedded in or included with the images) input object indicators corresponding to the products  112 . The preprocessor  200  is also configured to register the above-mentioned images to a common frame of reference, such as a coordinate system established within the retail facility. The control application  128  also includes a candidate detector  204  configured to select candidate subsets of object indicators (e.g., object indicators likely to correspond to products  112 , rather than to be false detections of products) in adjacent images among a set of the images. A cluster detector  208  is configured to select clusters of the input object indicators that likely depict the same product  112 , and an output generator  212  is configured to generate output object indicators—corresponding to object indicators assessed as being sufficiently likely to depict products  112  on the shelf  110 —from the clusters. 
     The functionality of the control application  128  will now be described in greater detail. Turning to  FIG. 3 , a method  300  of object detection is shown. The method  300  will be described in conjunction with its performance on the system  100  and with reference to the components illustrated in  FIG. 2C . As noted earlier, additionally, in other examples, some or all of the method  300  is performed by the components illustrated in  FIG. 2B . 
     At block  305 , the controller  120 , and in particular the preprocessor  200 , is configured to obtain a set of images depicting overlapping regions of an area containing a plurality of objects. The images obtained at block  305  are, for example, captured by the apparatus  103  and stored in the repository  132 . The preprocessor  200  is therefore configured, in the above example, to obtain the image by retrieving the image from the repository  132 . The process of obtaining the images at block  305  can also, in some embodiments, include the transmission of instructions to the apparatus  103  to capture the images. 
       FIGS. 4A and 4B  depict example sets of images. In particular, a first set of images  400 - 1  and  400 - 2  (which may therefore also be referred to as a set  400  of images) and a second set of images  404 - 1  and  404 - 2  (which may therefore also be referred to as a set  404  of images) are shown overlaid on a shelf module  110  to indicate the areas of the shelf  110  that each image depicts. As shown in  FIG. 4A , the images of the set  400  depict overlapping regions of a first area of the shelf  110 , and the images of the set  404  depict overlapping regions of a second area of the shelf  110 . The first and second areas themselves also overlap, as will be discussed in greater detail below. 
     More specifically, in the present example, the images within a given set (e.g., the images  400 - 1  and  400 - 2 ) depict regions of the shelf  110  that overlap in a direction substantially perpendicular to a direction of travel  408  of the apparatus  103  as the apparatus  103  travels along the shelf module  110  during image capture. The images of sequential sets, meanwhile, depict areas of the shelf  110  that are adjacent to each other, and in the present example overlap, in a direction substantially parallel to the direction of travel  408 . Thus, in the present example, the images  400 - 1  and  400 - 2  overlap vertically, and the images  404 - 1  and  404 - 2  overlap vertically. The area depicted by the images  400 - 1  and  400 - 2 , meanwhile, overlaps horizontally with the area depicted by the images  404 - 1  and  404 - 2 . 
     As seen in  FIG. 4A , the images  400  and  404  depict products  112  supported by the shelf module  110 . In particular, as shown in  FIG. 4B  which shows the images  400 - 1 ,  400 - 2  and  404 - 1  in isolation (the image  404 - 2  is omitted because it does not depict any products  112 ), the images together depict products  112 - 1 ,  112 - 2 ,  112 - 3  and  112 - 4 . Certain products are depicted fully in more than one image; for example, the product  112 - 3  is depicted in the images  400 - 1  and  404 - 1 . Further, certain products  112  are depicted partially in more than one image, but may not be depicted fully in any single image. For example, the product  112 - 1  is depicted partially in the image  400 - 1  and partially in the image  404 - 1 . As will be discussed in greater detail below, the control application  128  is configured to implement various functions to account for the repeated depiction (whether full or partial) of products  112  in the images  400  and  404  to obtain and refine product identifications. 
     The sets of images obtained at block  305  include a plurality of input object indicators. The input object indicators may be included as a layer of additional data in each image file, which may therefore be rendered visually as in  FIG. 4B . In other examples, however, the input object indicators may be obtained at block  305  as metadata or a separate file accompanying each image  400  and  404 . The input object indicators are obtained from a product recognition engine (e.g. included in the control application  128  or as a separate component of the server  101 ). The product recognition engine is configured to compare various image features of each image  400  and  404  to a database of product models and to select product models having image features that match those in the images. For each selected product model, the product recognition engine is configured to insert into the images or otherwise associate with the images an input object indicator. In other words, an input object indicator contains data defining a location within an image at which the product recognition engine detected a product  112 , and also identify that product  112 . 
     As seen in  FIG. 4B , each image includes (i.e. contains or is otherwise associated with) a number of input object indicators  412 . Specifically, the image  400 - 1  includes input object indicators  412   a ,  412   b ,  412   c  and  412   d ; the image  400 - 2  includes input object indicators  412   e  and  412   f ; and the image  404 - 1  includes object indicators  412   g  and  412   h . Each input object indicator  412  defines an input bounding box, which is illustrated in dashed lines in  FIG. 4B . Each input object indicator  412  also includes an object identifier. The object identifier is an identifier of the one of the above-mentioned product models selected by the product recognition engine as most matching the feature of the image within the bounding box. The object identifier may be, for example, a stock keeping unit (SKU) identification code or other suitable identifier. Each input object indicator also includes an input confidence level value, indicating a confidence assigned by the product recognition engine that the image features within the bounding box actually depict the input object identifier. Thus, for example, the input object indicator  400   f  indicates that the image features within the corresponding bounding box correspond to the product identifier “Y” with a confidence of 94%. The confidence values need not be expressed in percentages in other examples. 
     As will be apparent from  FIGS. 4A and 4B , the product recognition engine may identify products  112  where none are actually depicted in the images  400  and  404 . Other sources of error may also be introduced by the product recognition engine, such as selection of an incorrect object identifier, the detection of multiple objects where only one is present (e.g. as with the input object indicators  412   b  and  412   c ). 
     The preprocessor  200  is also configured to register the images (and therefore the associated input object indicators) obtained at block  305  to a common frame of reference, such as a coordinate system previously established within the retail facility. Such registration, as will be apparent to those skilled in the art, permits the contents of the images and input object indicators, to be compared to one another. The specific implementation of the registration to the common frame of reference is not the subject of the present disclosure, and is therefore not discussed herein; various suitable registration operations may be applied by the preprocessor  200 . 
     Returning to  FIG. 3 , at block  310 , the candidate detector  204  is configured to identify candidate subsets among the input object indicators obtained at block  305 . More specifically, the detector  204  identifies candidate subsets in adjacent ones of each set of images. That is, with reference to the images of  FIGS. 4A and 4B , candidate subsets are identified in the images  400 - 1  and  400 - 2  separately from the images  404 - 1  and  404 - 2 . Indeed, the remainder of the performance of the method  300  is specific to a particular set of images unless otherwise specified below. 
     Each candidate subset identified at block  310  has a member in one of the images in a set, and a member in an adjacent image of the same set. The members of the candidate subset also have input bounding boxes that overlap in the common frame of reference, as well as a common object identifier. In general, the candidate subsets identified at block  310  represent objects (such as products  112 ) that may be only partially depicted by each individual image, but that are nevertheless fully depicted by two images together. 
     Turning to  FIG. 5 , a method  500  of performing block  310  is shown, as performed by the detector  204 . At block  505 , the detector  204  is configured to select an image pair from the relevant set of images. In the present example, each set  400 ,  404  of images includes only one pair of images. However, in other examples each set may include a larger number of vertically-stacked images. In any event, in the present example performance of the method  500  the detector  204  is configured to select the images  400 - 1  and  400 - 2 , being an adjacent pair of images in the same set. 
     At block  510 , the detector  204  is configured to identify an input object indicator with a bounding box that coincides with an edge of the corresponding image, or is within a predetermined threshold distance of the edge. The edge in question is the edge of the first image in the pair selected at block  505  that is closest to the second image in the pair selected at block  505 . Referring to  FIG. 6A , in the present example, in which the images  400 - 1  and  400 - 2  are selected at block  505 , the edge assessed at block  510  is the bottom edge  600 - 1  of the image  400 - 1 . 
     In other words, at block  510  the detector  204  is configured to identify one of the input indicators in the image  400 - 1  that coincides with the edge  600 - 1 . As will be apparent, the bounding box of input indicator  412   a  coincides with the edge  600 - 1  of the image  400 - 1 . At block  515 , the detector  204  is configured to determine whether the adjacent image (i.e., the image  400 - 2  in the present example) includes a matching object indicator at an edge thereof. Referring again to  FIG. 6A , the detector  204  is configured to determine whether the image  400 - 2  contains an object indicator  412  at the edge  600 - 2  that matches the indicator  412   a . In the context of block  510 , an input object indicator  412  is considered to match another when there is at least some overlap of the respective bounding boxes of the indicators  412 , and when the indicators  412  share a common object identifier. As seen in  FIG. 6A , the input object indicator  412   e  coincides with the edge  600 - 2  of the image  400 - 2 , and includes the same object identifier “V” (see  FIG. 4B ) as the input object indicator  412   a . Further, in the common frame of reference the bounding boxes of the indicators  412   a  and  412   e  overlap, as shown in  FIG. 6A . In some examples, a minimum overlap threshold may be required at block  510 . 
     Returning to  FIG. 5 , when the determination at block  515  is negative, the detector  204  discards the input object indicator  412  identified at block  510 . That is, the input object indicator  412  is deleted from the data obtained at block  305 , and is not employed in the remainder of the processing discussed below. The input object indicator  412  is discarded at block  520  because, based on the knowledge that the images  400 - 1  and  400 - 2  overlap vertically, any object that is correctly detected at the edge of one image should also be at least partially depicted (and therefore detected by the product recognition engine) in the adjacent image. When no adjacent detection is made in the adjacent image, the detection in the first image is likely to be a false positive detection generated by the product recognition engine. 
     When, instead, the determination at block  515  is affirmative, as is the case with the input object indicators  412   a  and  412   e , the detector  204  proceeds to block  525 , at which the indicators  412   a  and  412   e  are added to a candidate subset. The candidate subset may be, for example, a list of identifiers of the indicators  412  for further processing as discussed below. Having added the indicators  412   a  and  412   e , the detector  204  is configured to determine at block  530  whether further indicators  412  remain to be processed in the image  400 - 1 . In the present example, the determination is affirmative, as the indicator  412   d  has not been processed. Therefore, blocks  510  and  515  are repeated for the indicator  412   d . As is evident from  FIG. 6A , the image  400 - 2  does not contain an input object indicator  412  that matches the input object indicator  412   d . The indicator  412   d  is therefore discarded at block  520 . The remaining set of input object indicators  412  after the performance of the method  500  is shown in  FIG. 6B , in which the indicator  412   d  has been discarded. 
     When the determination at block  530  is negative, the detector  204  is configured to determine whether additional image pairs remain to be processed in the set. In the present example, the determination is negative. In other examples, however, the set may contain three or more images, in which case the method  500  is repeated, with the pair of images consisting of the image  400 - 2  and the next vertically arranged image below the image  400 - 2 . 
     Returning to  FIG. 3 , at block  315  the candidate detector  204  is further configured to adjust the confidence level values of any candidate subsets identified at block  310 . As will be apparent from  FIG. 4B , the input object indicators  412  that are identified as candidate subsets will tend to correspond to products  112  that are only partially depicted in each image  400  or  404 . As the product recognition engine mentioned earlier operates on each image independently, such partially depicted objects may still be detected, but assigned lower confidence level values than if they were fully depicted, as a result of certain product features being absent from each image. Thus, for example, the confidence levels for the product  112 - 1  are 59% and 30% in the input object indicators  412   a  and  412   e , respectively. To reduce the likelihood of discarding the indicators  412   a  and  412   e  at a later stage under the application of a confidence level threshold, the confidence levels contained in the indicators  412   a  and  412   e  are adjusted upwards to simulate the confidence levels expected had the product  112 - 1  been fully depicted in each image  400 . 
     To that end, turning to  FIG. 7A , the candidate detector  204  is configured to perform the method  700 , in which at block  705  the detector  204  is configured to determine a degree of occlusion for each candidate in the subset via the method  500 . The degree of occlusion is determined relative to reference data for the corresponding object identifier. Thus, as shown in  FIG. 7B , the detector  204  is configured to retrieve a reference bounding box  720  from the repository  132 , and to determine a proportion  724  of the reference bounding box that is not accounted for by the bounding box of the input object indicator  412   a , which is indicative of a portion of the product “V” not being depicted in the image  400 - 1 . Based on the area of the proportion  724  relative to the total area of the reference bounding box  720 , the detector  204  is configured to adjust the confidence level value of the indicator  412   a  at block  710 . 
     In the present example, referring to  FIG. 7C , the adjustment at block  710  is made based on a stored relationship between degrees of occlusion and confidence levels produced by the product recognition engine. For example, a set of measurements may be obtained in which predetermined portions of a product  112  are occluded, an image of the product is captured and processed by the product recognition engine, and the resulting confidence level value is stored in conjunction with the predetermined degree of occlusion. Based on the relationship between occlusion and confidence level established by a sufficient number of such measurements, at block  710  the detector  204  can be configured to select a new confidence level value using the degree of occlusion from block  705  as input. When the degree of occlusion and the initial confidence level falls on the line of  FIG. 7C , for example, the adjusted confidence level set at block  710  may be the maximum measured confidence level (e.g. about 95%, in  FIG. 7C ). When the degree of occlusion and the initial confidence level fall in the area underneath the line, however the detector  204  may be configured to apply a ratio to the maximum confidence level corresponding to the ratio of the initial confidence level to the expected confidence level (from the graph of  FIG. 7C ) based on the degree of occlusion from block  705 . The performance of the method  700  is then repeated for the input object indicator  412   e . As a result, the input object indicators  412   a  and  412   e  are assigned new confidence level values (e.g. 85% for the indicator  412   a  and 81% for the indicator  412   e ). 
     Returning to  FIG. 3 , at block  317  the detector  304  may also be configured to adjust the bounding boxes of the indicators  412   a  and  412   e , for example by replacing the bounding boxes with the reference bounding box  720 . In other embodiments, block  317  may be omitted. 
     At block  320 , the cluster detector  208  is configured to select clusters of the input object indicators  412 . Each cluster, as discussed in greater detail below, contains indicators  412  with confidence levels (whether the initial levels shown in  FIG. 4B  if no adjustments were made, or adjusted confidence levels if applicable) that satisfy a predefined minimum input confidence threshold. The input object indicators of each cluster also have a common object identifier and a degree of overlap that satisfies a predefined threshold. 
     Turning to  FIG. 8 , a method  800  of cluster generation is illustrated, as performed by the cluster detector  208 . As will now be apparent, the identification of candidate subsets of indicators  412  and associated removal of indicators  412  that do not form part of any candidate subsets serves to eliminate some false positive detections, while reducing the likelihood of correct detections being eliminated due to partial depiction of the underlying objects. As will be seen below, the selection of clusters and the generation of output object indicators therefrom serves to eliminate further false positives detections. 
     At block  805 , the cluster detector  208  is configured to generate a ranked list of the input object indicators  412 , as they appear following the processing steps discussed above (e.g., omitting any indicators  412  discard at block  520 , and ranking indicators  412  based on adjusted confidence levels as applicable). The indicators  412  are arranged in the list generated at block  805  based on their confidence levels. Indicators having confidence levels below a predefined threshold (e.g., 65%) may also be omitted from the ranked list. Table 1, below, illustrates an example list generated at block  805  for the indicators  412  from the images  400 - 1  and  400 - 2 . 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Ranked List of Input Object Indicators 
               
            
           
           
               
               
               
               
            
               
                   
                 Indicator ID 
                 Product ID 
                 Confidence 
               
               
                   
                   
               
               
                   
                 412f 
                 Y 
                 94 
               
               
                   
                 412a 
                 V 
                 85 
               
               
                   
                 412e 
                 V 
                 81 
               
               
                   
                 412b 
                 W 
                 79 
               
               
                   
                 412c 
                 W 
                 71 
               
               
                   
                   
               
            
           
         
       
     
     The list generated at block  805  need not include all the information shown above, which is included primarily for illustrative purposes. Further, the list need not be generated in the tabular format shown above. At block  810 , the cluster detector  208  is configured to select a primary input object indicator from the list, and to initiate a cluster with the selected primary input object indicator. The primary object indicator selected is the indicator  412  having the highest position in the list (i.e., the highest confidence level), and which has not yet been processed. Thus, in the present example performance of the method  800 , the indicator  412   f  is selected at block  810 . 
     At block  815 , the cluster detector  208  is configured to determine whether any secondary input object indicators remain to be processed. A secondary indicator  412  is an indicator  412  in the list from block  805  that has a common object identifier with the primary object indicator selected at block  810 . When the determination at block  815  is affirmative, the cluster detector  208  is configured to perform blocks  820 ,  825  and  830 , as discussed further below. In the present example performance, however, the determination at block  815  is negative (there are no other indicators  412  containing the object identifier “Y”). The cluster detector  208  therefore proceeds to block  835 , and determines whether the primary indicator is occluded beyond a predefined threshold by prior output indicators. As no output indicators have been generated, the determination is negative, and the cluster detector  208  is configured to proceed to block  840  to determine whether further primary input object indicators remain to be processed in the list generated at block  805 . In the present example, the determination is affirmative, and the cluster detector  208  therefore returns to block  810 , having selected a first cluster containing only the input object indicator  412   f  Further processing of the clusters will be described further below in connection with block  325  of the method  300 . 
     In the second performance of block  810 , the next indicator  412  having the next highest confidence level (the indicator  412   a ) is selected as a primary input object indicator. At block  815 , the determination is affirmative, because the indicator  412   e  has the same object identifier as the indicator  412   a . At block  820 , therefore, the cluster detector  208  is configured to select the indicator  412   e  as a secondary indicator. At block  825 , the cluster detector  208  is configured to determine whether a degree of overlap between the primary and secondary indicators exceeds a predefined threshold. For example, the detector  208  can be configured to determine the ratio of the area of intersection between the primary and secondary indicators to the area of the union of the primary and secondary indicators, and to compare the ratio to the threshold (e.g., 70%). The degree of overlap threshold serves to indicate not only whether the primary and secondary input object indicators overlap, but also whether the overlapping area represents a sufficiently large portion of the total area covered by the bounding boxes of the primary and secondary indicators. When the determination at block  825  is negative, the secondary object indicator is not added to the cluster initiated at block  810 , and the list is searched for further secondary input object indicators. When the determination at block  825  is affirmative, however, the cluster detector  208  proceeds to block  830 . 
     In the present example performance, it is assumed that the bounding boxes of the indicators  412   a  and  412   e  were updated at block  317  to align with the reference box  720  shown in  FIG. 7B . Therefore, the determination at block  825  is affirmative, and at block  830 , at which the indicator  412   e  is added to the cluster initiated with the indicator  412   a . In some examples, adding a secondary input object indicator to a cluster includes adding an identifier of the cluster to a listing of indicators  412  included in the cluster. In other examples, however, the secondary input object indicator may be added to the cluster by simply removing the secondary input object indicator from the ranked list. In such examples, the primary indicator is taken to represent both itself and the secondary indicator in the later processing discussed below. 
     The above process is repeated for the remaining indicators in the list of Table 1. As will now be apparent, a third cluster is generated including the indicators  412   b  and  412   c  (for example, by discarding the indicator  412   c  and maintaining the indicator  412   b  as a representative member of the cluster). 
     When the determination at block  840  is negative, the performance of the method  300  continues at block  325 . At block  325 , the output generator  212  is configured to generate a single output object indicator for each cluster selected at block  320 . Thus, in the present example performance of the method  300 , at block  325  the output generator generates three output object indicators, one each corresponding to the clusters represented by the input object indicators  412   a ,  412   b  and  412   f  Each output object indicator includes the same object identifier as the object identifier of the cluster. Each output object indicator also includes a bounding box and confidence level derived from the cluster. In the present example, in which secondary indicators are discarded at block  830 , the bounding boxes and confidence levels of the output object indicators are simply taken from the primary input object indicator of each cluster. In other examples, however, one or both of the bounding box and the confidence level of the output object indicator are derived from a combination of the primary and secondary input object indicators in the cluster. For example, an average of the primary and (one or more) secondary indicator confidence levels may be employed as the output confidence level. Further, the output bounding box may be a union or an intersection of the primary and secondary bounding boxes of the cluster. 
     Turning to  FIG. 9A , a set of three output object indicators  900   a ,  900   b  and  900   f  are shown, corresponding to the three clusters discussed above and illustrated overlaid on the corresponding products  112  on the shelf  110 . 
     Following the performance of block  325 , the control application  128  is configured to determine whether further sets of images remain to be processed. In the present example, the determination at block  330  is affirmative, as the images  404 - 1  and  404 - 2  have not been processed. The method  300  is therefore repeated for the images  404 - 1  and  404 - 2 . Of particular note, the cluster detector  208  is configured, at block  805 , to include previously generated output object indicators in the ranked list. Thus, in connection with the second performance of the method  300 , the ranked list is as shown below in Table 2. 
     
       
         
           
               
             
               
                 TABLE 2 
               
             
            
               
                   
               
               
                 Ranked List of Input Object Indicators 
               
            
           
           
               
               
               
               
            
               
                   
                 Indicator ID 
                 Product ID 
                 Confidence 
               
               
                   
                   
               
               
                   
                 900f 
                 Y 
                 94 
               
               
                   
                 412h 
                 Z 
                 91 
               
               
                   
                 900a 
                 V 
                 85 
               
               
                   
                 412g 
                 W 
                 82 
               
               
                   
                 900b 
                 W 
                 79 
               
               
                   
                   
               
            
           
         
       
     
     As will now be apparent, traversing the method  800  for the above ranked list results in the generation of four clusters, corresponding to the indicators  900   f ,  412   h ,  900   a , and  412   g . Of note, the output object indicator  900   b  is added as a secondary object indicator to a cluster initiated with the input object indicator  412   g  (which has a higher confidence level than the output object indicator  900   b ). 
     At block  835 , as mentioned earlier, each cluster (e.g., each primary object indicator) is evaluated for a degree of occlusion by any previous output indicators, irrespective of object identifiers. The cluster detector  208  is configured to generate a mask consisting of the union of all output object indicators (i.e.,  900   a ,  900   b  and  900   f  in the present example), and to determine a degree (e.g., a percentage) to which the current cluster is occluded by the above-mentioned mask. For example, the degree of occlusion may be determined as the ratio of the intersection between the above mask and the area of the primary input object indicator to the area of the primary input object indicator. If the degree of occlusion exceeds a predefined threshold (e.g., 60%), then the cluster is discarded at block  845 . Such a degree of occlusion indicates either a false positive detection, or a disordered shelf  110  in which the products  112  are misaligned and therefore occlude one another significantly. On the assumption that such a degree of disorder is typically rare, the cluster is discarded as being a false positive detection by the product recognition engine. In other embodiments, blocks  835  and  845  may be omitted. 
     Referring to  FIG. 9B , an updated set of output object indicators is illustrated, including an output object indicator  900   h , and an output object indicator  900   b - 2 , which has replaced the indicator  900   b  shown in  FIG. 9A . 
     Responsive to a negative determination at block  325 , the control application  128  is configured to store the output object indicators, for example in the repository  132 , for rendering on a display, for further processing to derive object status information, and the like. 
     Variations to the above systems and methods are contemplated. For example, in some embodiments, the candidate detector  204  is configured to implement one or more validation operations at block  510 . For example, the bounding boxes of the input object indicators  412  can be compared to a shelf edge location obtained by the detector  204 , and any indicators  412  that overlap with the shelf edge location to a degree greater than a threshold may be discarded. 
     In further embodiments, the adjustment of confidence levels (block  315 ) as well as the adjustment of bounding boxes (block  317 ) may be performed simultaneously with block  525 , rather than after the completion of the method  300 . 
     In the foregoing specification, specific embodiments have been described. However, one of ordinary skill in the art appreciates that various modifications and changes can be made without departing from the scope of the invention as set forth in the claims below. Accordingly, the specification and figures are to be regarded in an illustrative rather than a restrictive sense, and all such modifications are intended to be included within the scope of present teachings. 
     The benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential features or elements of any or all the claims. The invention is defined solely by the appended claims including any amendments made during the pendency of this application and all equivalents of those claims as issued. 
     Moreover in this document, relational terms such as first and second, top and bottom, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. The terms “comprises,” “comprising,” “has”, “having,” “includes”, “including,” “contains”, “containing” or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises, has, includes, contains a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by “comprises . . . a”, “has . . . a”, “includes . . . a”, “contains . . . a” does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises, has, includes, contains the element. The terms “a” and “an” are defined as one or more unless explicitly stated otherwise herein. The terms “substantially”, “essentially”, “approximately”, “about” or any other version thereof, are defined as being close to as understood by one of ordinary skill in the art, and in one non-limiting embodiment the term is defined to be within 10%, in another embodiment within 5%, in another embodiment within 1% and in another embodiment within 0.5%. The term “coupled” as used herein is defined as connected, although not necessarily directly and not necessarily mechanically. A device or structure that is “configured” in a certain way is configured in at least that way, but may also be configured in ways that are not listed. 
     It will be appreciated that some embodiments may be comprised of one or more generic or specialized processors (or “processing devices”) such as microprocessors, digital signal processors, customized processors and field programmable gate arrays (FPGAs) and unique stored program instructions (including both software and firmware) that control the one or more processors to implement, in conjunction with certain non-processor circuits, some, most, or all of the functions of the method and/or apparatus described herein. Alternatively, some or all functions could be implemented by a state machine that has no stored program instructions, or in one or more application specific integrated circuits (ASICs), in which each function or some combinations of certain of the functions are implemented as custom logic. Of course, a combination of the two approaches could be used. 
     Moreover, an embodiment can be implemented as a computer-readable storage medium having computer readable code stored thereon for programming a computer (e.g., comprising a processor) to perform a method as described and claimed herein. Examples of such computer-readable storage mediums include, but are not limited to, a hard disk, a CD-ROM, an optical storage device, a magnetic storage device, a ROM (Read Only Memory), a PROM (Programmable Read Only Memory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory) and a Flash memory. Further, it is expected that one of ordinary skill, notwithstanding possibly significant effort and many design choices motivated by, for example, available time, current technology, and economic considerations, when guided by the concepts and principles disclosed herein will be readily capable of generating such software instructions and programs and ICs with minimal experimentation. 
     The Abstract of the Disclosure is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, it can be seen that various features are grouped together in various embodiments for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as a separately claimed subject matter.