Patent Publication Number: US-10769472-B2

Title: Method and system counting plurality of objects placed in a region

Description:
TECHNICAL FIELD 
     The present subject matter is, in general, related to image analysis and more particularly, but not exclusively, to a method and system for counting plurality of objects placed in a region by means of image processing techniques. 
     BACKGROUND 
     Presently, in most of large-scale factories and warehouses, counting number of objects such as goods, raw materials and the like, lying at manufacturing units or at distribution units has become a major problem, as manual counting of the objects consumes a lot of time, and is a labor-intensive process which is prone to manual errors. In some instances, it may be required that the objects that are being loaded onto or being unloaded from a carrying vehicle like truck, need to be counted before and/or after transportation. Additionally, for verification and auditing purposes, it may also be required to count the number of objects at both ends of the transportation, i.e., where the objects are being loaded and where the objects are getting unloaded. However, the manual counting process is a very tedious and error-prone task, due to which overall transportation time may be increased. 
     The existing methods for automatically counting the number of objects placed in a location include analyzing an image of the location/objects and then returning a count of the objects identified in the image. However, the existing methods may return a wrong count of the objects when the objects are present in a cluttered or a distorted background. Further, the existing methods fail to take accurate count of the objects in a location, when the objects are partially visible in the image due to occlusion or incomplete image coverage, or when ends of multiple objects appear to be merged in the image due to shadow or unfavorable lighting conditions. Hence, counting the number of distinct objects accurately in such scenarios may be extremely difficult. 
     The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art. 
     SUMMARY 
     One or more shortcomings of the prior art may be overcome, and additional advantages may be provided through the present disclosure. Additional features and advantages may be realized through the techniques of the present disclosure. Other embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed disclosure. 
     Disclosed herein is a method for counting plurality of objects placed in a region. The method comprises partitioning, by an object counting system, an image of the region, comprising the plurality of objects, into one or more segments based on depth of each of the plurality of objects. The image of the region is received from an image capturing unit associated with the object counting system. Further, the method comprises identifying one or more object regions in each of the one or more segments. Upon identifying the one or more object regions, the method comprises determining shape of each of the plurality of objects in each of the one or more object regions of each of the one or more segments. Further, the method comprises validating the shape of each of the plurality of objects based on comparison of the shape of each of the plurality of objects with predetermined shapes. Finally, the method comprises aggregating count of the plurality of objects of each shape in each of the one or more segments for determining count of the plurality of objects in the region. 
     Further, the present disclosure relates to an object counting system for counting plurality of objects placed in a region. The object counting system comprises a processor and a memory. The memory is communicatively coupled to the processor and stores processor-executable instructions, which on execution cause the processor to partition an image of the region, comprising the plurality of objects, into one or more segments based on depth of each of the plurality of objects. The image of the region is received from an image capturing unit associated with the object counting system. Further, the instructions cause the processor to identify one or more object regions in each of the one or more segments. Upon identifying the one or more object regions, the instructions cause the processor to determine shape of each of the plurality of objects in each of the one or more object regions of each of the one or more segments. Furthermore, the instructions cause the processor to validate the shape of each of the plurality of objects based on comparison of the shape of each of the plurality of objects with predetermined shapes. Finally, the instructions cause the processor to aggregate count of the plurality of objects of each shape in each of the one or more segments to determine count of the plurality of objects in the region. 
     Furthermore, the present disclosure includes a non-transitory computer readable medium including instructions stored thereon that when processed by at least one processor cause an object counting system to perform operations including partitioning an image of the region, comprising the plurality of objects, into one or more segments based on depth of each of the plurality of objects. The image of the region is received from an image capturing unit associated with the object counting system. Further, the instructions cause the processor to identify one or more object regions in each of the one or more segments. Upon identifying the one or more object regions, the instructions cause the processor to determine shape of each of the plurality of objects in each of the one or more object regions of each of the one or more segments. Further, the instructions cause the processor to validate the shape of each of the plurality of objects based on comparison of the shape of each of the plurality of objects with predetermined shapes. Finally, the instructions cause the processor to aggregate count of the plurality of objects of each shape in each of the one or more segments for determining count of the plurality of objects in the region. 
     The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and regarding the accompanying figures, in which: 
         FIG. 1  illustrates an exemplary environment for counting plurality of objects placed in a region in accordance with some embodiments of the present disclosure; 
         FIG. 2  shows a detailed block diagram illustrating an object counting system in accordance with some embodiments of the present disclosure; 
         FIGS. 3A-3I  illustrate schematic representations indicating an exemplary counting process of objects placed in a region in accordance with the present disclosure; 
         FIG. 4  shows a flowchart illustrating a method of counting plurality of objects placed in a region in accordance with some embodiments of the present disclosure; and 
         FIG. 5  illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure. 
     
    
    
     It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether such computer or processor is explicitly shown. 
     DETAILED DESCRIPTION 
     In the present document, the word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment or implementation of the present subject matter described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments. 
     While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the specific forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the scope of the disclosure. 
     The terms “comprises”, “comprising”, “includes”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device, or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises . . . a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method. 
     The present disclosure relates to a method and an object counting system for counting plurality of objects placed in a region. In an embodiment, the method of present disclosure may be capable of identifying and counting distinct, multi-dimensional, and varied shaped objects from a distorted background by capturing an image of a region in which the plurality of objects to be counted are placed. Further, the method of present disclosure may also be capable of determining the count of the plurality of objects which are placed at different and/or non-uniform depth levels in the region. 
     In an embodiment, the present disclosure includes partitioning the image of the plurality of objects into one or more segments based on depth of the plurality of objects. Subsequently, each of the one or more segments may be analyzed to identify one or more object regions and one or more non-object regions. Further, each of the one or more object regions may be processed for determining shape of each of the plurality of objects present in the one or more object regions. Thereafter, a pre-trained machine learning technique may be used to validate the determined shape of each of the plurality of objects by comparing the shape of each of the plurality of objects with one or more predetermined shapes. Upon validating the shape of each of the plurality of objects, the count of the plurality of objects in the region may be computed as an aggregate of the count of each object belonging to each of the shape types. 
     In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense. 
       FIG. 1  illustrates an exemplary environment  100  for counting plurality of objects placed in a region in accordance with some embodiments of the present disclosure. 
     In an embodiment, the environment  100  may include an object counting system  103  and an image capturing unit  101 . In an implementation, the object counting system  103  may be a computing device such as a smartphone, a desktop, a laptop, a Personal Digital Assistant (PDA) and the like, which may be configured to count plurality of objects placed in a region by analyzing and processing an image  102  of the plurality of objects placed in the region. Alternatively, the object counting system  103  may be used as an application integrated in any of the computing devices stated above. 
     In an embodiment, the image capturing unit  101  may be a digital camera. Alternatively, a smartphone having an embedded digital camera may also be used for capturing the image  102  of the region. In some implementations, the image capturing unit  101  may include an array of multiple image capturing sensors, each consisting of a conventional Red Green Blue (RGB) sensor, and a depth/range sensor that may estimate depth values of the plurality of objects. Further, the depth sensor may be aligned with the RGB sensors, such that, for each pixel of the image  102  captured by the RGB sensor, there is an associated depth value estimated from the depth sensor. 
     In an embodiment, before transmitting the image  102  to the object counting system  103 , the image capturing unit  101  may analyze the captured image  102  to verify whether the captured image  102  covers each of the plurality of objects placed in the region, as a single image  102  of the region might not cover each of the plurality of objects placed in the region. In an embodiment, when the image capturing unit  101  detects that a complete image  102  of the region has not been captured, the image capturing unit  101  may dynamically activate an assistive panorama image capturing mechanism. The assistive panorama image capturing mechanism may provide feedback to user, indicating the user to move the image capturing unit  101  in a direction of the one or more objects which are not covered in the already captured image  102 . 
     In other words, after capturing the image  102  of the region, the image capturing unit  101  may verify whether each of the plurality of objects are within a predefined boundary in the image  102 . Suppose, if one or more of the plurality of objects are detected to be outside the predefined boundary, then the image capturing unit  101  may capture one or more additional images of the region to ensure that each of the plurality of objects placed in the region are covered in the image  102 . Further, upon capturing the one or more additional images, the image capturing unit  101  may combine/merge each of the one or more additional images into a single image  102  of the region, which may be subsequently transmitted to the object counting system  103  for further processing. 
     In some implementations, the image capturing unit  101  may capture the image  102  of the region (i.e. the image  102  and the one or more additional images, when required) in a field-of-view at a predefined rate, for example at a rate of 5 to 30 frames per second. The rate at which the image capturing unit  101  captures the image  102  of the region may be dynamically set by a user while capturing the image  102  of the region. In some implementations, the image capturing unit  101  and the object counting system  103  may be embedded within a single computing device such as a smartphone. Alternatively, the object counting system  103  may be located at a remote location, wherein the image capturing unit  101  captures the image  102  of the region and transmits the captured image  102  to the object counting system  103  over a wired and/or a wireless communication network. 
     In an embodiment, upon receiving the image  102  of the region, the object counting system  103  may partition the image  102  into one or more segments based on the depth of each of the plurality of objects in the image  102 . Further, the object counting system  103  may identify one or more object regions within each of the one or more segments by analyzing the image  102  using a pre-trained machine learning classifier, which classifies each region within each of the one or more segments as the one or more object regions and one or more non-object regions. The one or more object regions may be the regions which comprise one or more of the plurality of objects. Similarly, the one or more non-object regions are the regions that do not comprise any of the plurality of objects. In an embodiment, the object counting system  103  may eliminate the one or more non-object regions from further processing, in order to enhance speed of the object counting process. 
     In an embodiment, upon identifying the one or more object regions, the object counting system  103  may determine shape of each of the plurality of objects in each of the one or more object regions based on one or more shape-specific parameters from each of the one or more regions. As an example, the shape of the plurality of objects may be circular, rectangular, polygonal and the like. Similarly, the one or more shape-specific parameters associated with the shape may include, without limiting to, radius of the shape (when the object is in circular shape), or length of sides/diagonals (when the object is in polygonal shape). 
     In an embodiment, upon determining the shape of each of the plurality of objects, the object counting system  103  may validate the shape of each of the plurality of objects by comparing the shape of each of the plurality of objects with predetermined shapes. The predetermined shapes may include each of one or more shapes/forms in which the plurality of objects may be present in the region. As an example, the predetermined shapes may be circular, spherical, cylindrical, polygonal and the like. 
     In an embodiment, upon validating the shape of each of the plurality of objects, the object counting system  103  may compute count of the one or more objects of each shape in each of the one or more object regions, and subsequently may aggregate the count of each of the plurality of objects of each shape for determining the count  105  of the plurality of objects in the region. In an embodiment, upon determining the count  105  of the plurality of objects, the object counting system  103  may display/notify the count  105  of the plurality of objects to the user through a user interface associated with the user device and/or the object counting system  103 . 
       FIG. 2  shows a detailed block diagram illustrating an object counting system  103  in accordance with some embodiments of the present disclosure. 
     In an implementation, the object counting system  103  may include an I/O interface  201 , a processor  202 , and a memory  203 . The I/O interface  201  may be configured to communicate with an image capturing unit  101  associated with the object counting system  103  for receiving an image  102  of a region having plurality of objects to be counted. Further, the I/O interface  201  may be configured for providing the count  105  of the plurality of objects in the region to a user, through a user interface associated with the user device or the object counting system  103 . The processor  202  may be configured to perform one or more functions of the object counting system  103  while counting the plurality of objects in the image  102 . The memory  203  may be communicatively coupled to the processor  202 . 
     In some implementations, the object counting system  103  may include data  204  and modules  205  for performing various operations in accordance with embodiments of the present disclosure. In an embodiment, the data  204  may be stored within the memory  203  and may include information related to, without limiting to, the image  102 , one or more segments  206 , one or more object regions  207 , shape of plurality of objects  208  (hereinafter referred to as shape  208 ), predetermined shapes  210 , count of the plurality of objects  105  (hereinafter referred to as count  105 ), and other data  211 . 
     In some embodiments, the data  204  may be stored within the memory  203  in the form of various data structures. Additionally, the data  204  may be organized using data models, such as relational or hierarchical data models. The other data  211  may store various data, including spatial frequencies of the one or more object regions  207 , probability score associated with each shape  208 , pixel area and pixel indices related to space occupied by the plurality of objects, value of a Mutual Shape Co-occurrence Factor (MSCF), and other temporary data and files generated by one or more modules  205  for performing various functions of the object counting system  103 . 
     In an embodiment, the image  102  of the region may be captured by the image capturing unit  101  associated with the object counting system  103 , and may indicate position/arrangement, shape, color of each of the plurality of objects in the region. As an example, the image  102  may be in various forms including, but not limiting to, a colored image, a black and white image or a monochrome image. 
     In an embodiment, the one or more segments  206  may be obtained by partitioning the image  102  based on depth of the plurality of objects in the region. Partitioning the image  102  into the one or more segments  206  may help in distinctly identifying each of the plurality of objects of each depth. 
     In an embodiment, the one or more object regions  207  may be identified in each of the one or more segments  206  by detecting the plurality of objects in each of the one or more segments  206  using a pre-trained machine learning classifier configured in the object counting system  103 . Upon detecting the plurality of objects, the machine learning classifier may classify each region within each of the one or more segments  206  as one or more object regions  207  and one or more non-object regions. Subsequently, each of the one or more object regions  207  may be taken for further processing as each of the one or more object regions  207  indicate presence of the one or more objects in the one or more segments  206  of the image  102 . 
     In an embodiment, the shape  208  of the plurality of objects may be physical appearance and/or outline of the plurality of objects in the region. As an example, the shape  208  of the plurality of objects may include, but not limiting to, circular, rectangular, polygonal and the like. The shape  208  may be determined based on one or more shape-specific parameters extracted from each of the one or more object regions  207 . For example, the one or more shape-specific parameters associated with the shape  208  may include, without limiting to, radius of the shape  208  (when the object is in a circular shape), or length of sides/diagonals (when the object is in a polygonal shape). 
     In an embodiment, the predetermined shapes  210  may include, each of one or more shapes/forms in which the plurality of objects may be present in the region. As an example, the predetermined shapes  210  may be circular, spherical, cylindrical, polygonal and the like. The predetermined shapes  210  may be used for validating the shape  208  of each of the plurality of objects in each of the one or more object regions  207 . 
     In an embodiment, the count  105  of the plurality of objects may be obtained by aggregating the count  105  of the plurality of objects of each shape  208  in each of the one or more segments  206 . In other words, the count  105  of the plurality of objects in the region may be equal to sum of the total number of objects of each shape  208  in each of the one or more segments  206 . 
     In an embodiment, each of the data  204  stored in the object counting system  103  may be processed by one or more modules  205  of the object counting system  103 . In one implementation, the one or more modules  205  may be stored as a part of the processor  202 . In another implementation, the one or more modules  205  may be communicatively coupled to the processor  202  for performing one or more functions of the object counting system  103 . The modules  205  may include, without limiting to, an image partitioning module  213 , an object region identification module  215 , a shape determination module  217 , a shape validation module  219 , an object counting module  221  and other modules  223 . 
     As used herein, the term module refers to an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. In an embodiment, the other modules  223  may be used to perform various miscellaneous functionalities of the object counting system  103 . It will be appreciated that such modules  205  may be represented as a single module or a combination of different modules. 
     In an embodiment, the image partitioning module  213  may be used for partitioning the image  102  of the region into one or more segments  206 . The image partitioning module  213  may partition the image  102  based on depth of each of the plurality of objects in the image  102 . In an embodiment, the image  102  received from the image capturing unit  101  may consist of an RGB frame and a depth frame, which is augmented with color pixel values in the RGB frame. Thus, upon receiving the image  102 , the image partitioning module  213  may extract depth values from each depth frames and may store the depth values into a number of containers of different depth values for detecting the depth of each of the plurality of objects. Thereafter, one or more segments  206  of the image  102  may be generated according to the depth values stored in each of the containers, by suppressing all pixel values, corresponding to depth values lying outside the containers, into zeros. Thus, at the end of partitioning, there may be one or more segments  206  in each depth level, wherein each of the one or more segments  206  comprise one or more objects belonging to the same depth level. In an embodiment, partitioning the image  102  based on the depth of the plurality of objects may ensure that none of the plurality of objects remain undetected during the counting process, even when the plurality of objects is placed at varied depths in the region. 
     In an embodiment, the object region identification module  215  may be used for identifying one or more object regions  207  in each of the one or more segments  206  of the image  102 . In some implementations, the object region identification module  215  may be configured with a pre-trained machine learning classifier which may be capable of detecting one or more objects in each of the one or more segments  206 . In an embodiment, for each of the one or more segments  206  of the image  102 , the object region identification module  215  may iteratively scan entire region of each of the one or more segments  206  by considering one overlapping window of the one or more segments  206  at each iteration. Further, for each window position, the object identification module may apply the pre-trained machine learning classifier to classify the window as one of the object region or the non-object region. 
     For example, consider the image shown in  FIG. 3A , which is an image  102  of plurality of pipes arranged in storehouse. Now, when the image  102  is given as an input to the object region identification module  215  for identifying one or more pipe regions in the image  102 , the object region identification module  215  may identify each of the plurality of pipes in the image  102  and extract a region of pipes from the image  102  as shown in  FIG. 3B . Further, each of the non-pipe region may be eliminated, thereby extracting only the region of pipes (as indicated in  FIG. 3B ) for further processing by the object counting system  103 . In an embodiment, one or more of the plurality of pipes, whose frontal-face are not covered/recognized in the image  102  may be excluded from the region of pipes. 
     In an embodiment, the shape determination module  217  may be used for determining the shape  208  of each of the plurality of objects in each of the one or more object regions  207  of each of the one or more segments  206 . The shape determination module  217  may analyze each of the one or more object regions  207  for obtaining spatial frequencies of each of the one or more object regions  207  comprising the plurality of objects. Thereafter, the shape determination module  217  may generate a magnitude spectrum histogram of the spatial frequencies of each of the one or more object regions  207 . Further, the shape determination module  217  may determine a peak value of the magnitude spectrum histogram, which corresponds to one or more shape-specific parameters associated with the shapes  208 . Thereafter, the peak value of the magnitude spectrum histogram may be multiplied with a predetermined scaling factor for obtaining pixel coordinates corresponding to the plurality of objects. As an example, the one or more shape-specific parameters may include, without limiting to, parameters such as radius of the shape  208  (when the object is in circular shape), or length of sides/diagonals (when the object is in polygonal shape), and the like. The predetermined scaling factor may correspond to values of the shape-specific parameters. 
     In an embodiment, upon identifying the one or more shape-specific parameters, the shape determination module  217  may perform detection of shapes of each of the one or more object regions  207  by analyzing each of the one or more shape-specific parameters using a predetermined feature extraction technique such as Hough transform algorithm configured in the shape determination module  217 . As an example, when an input image  102  such as  FIG. 3C  is given as an input to the shape determination module  217 , the shape detection performed by the shape determination module  217  may be as shown in  FIG. 3D , wherein each of the one or more object regions  207  that correspond to circular object shapes (i.e. pipes) of a predetermined scaling factor are indicated as highlighted. 
     In an embodiment, the shape validation module  219  may be used for validating the shape  208  of each of the plurality of objects based on comparison of the shape  208  of each of the plurality of objects with predetermined shapes  210 . The shape validation module  219  may assign a probability score for the shape  208  of each of the plurality of objects by comparing each raw detection of the shapes with the predetermined shapes  210 . Further, the shape  208  of each of the plurality of objects may be considered to be valid when the probability score of the shape  208  is higher than a threshold probability score. As an example, the threshold probability score may be 0.80, which means that the shape  208  may be considered to be valid when the probability score of the shape  208  is higher than 0.80. 
     In an embodiment, when the raw detections of shapes, such as the detections shown in  FIG. 3D  are given to the shape validation module  219 , the shape validation module  219  may compute a probability score for each raw detection of the shapes, i.e., for each circle detected in  FIG. 3D , and may compare the probability scores with the threshold probability score for validating the shapes  208 . Finally, upon validation, only those raw detections of shapes whose probability score is higher than the threshold probability score may be considered valid and retained for counting, as shown in  FIG. 3E . 
     In an embodiment, the object counting module  221  may be used for counting the plurality of objects of each shape  208  in each of the one or more segments  206  for determining the count of the plurality of objects in the region. In an embodiment, when the image  102  comprises one or more objects of varied shapes, the object counting module  221  may initially count the number of objects belonging to each shape type, and then count  105  the plurality of objects in the region as an aggregate of the count of number of objects of varied shape types. As an example, consider a plurality of objects of different shape types namely, objects of shape of type 1  301  (circular objects), objects of shape type 2  303  (square shaped objects) and objects of shape type 3  305  (triangular objects) arranged in an object region  300 , as shown in  FIG. 3F . In an embodiment, upon detecting and validating the shape  208  of each of the plurality of objects in  FIG. 3F , the object counting module  221  may compute an individual count of the objects belonging to each shape type before computing the aggregate count  105  of all the objects present in the object region  300  as following:
 
Count of objects belonging to shape type 1=6
 
Count of objects belonging to shape type 2=5
 
Count of objects belonging to shape type 3=4
 
     Thereafter, the object counting module  221  may aggregate the count of objects of each shape type to determine the count  105  of all the objects present in the object region  300  as following: 
     
       
         
           
             
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     In an embodiment, the object counting system  103  may display/notify only the aggregate count  105  of the plurality of objects to a user. Alternatively, the object counting system may display/notify the count of objects of each shape type to the user, along with the aggregate count  105  of the plurality of objects in the object region  300 . 
     In an embodiment, the object counting system  103  may be configured to detect and eliminate one or more overlapping shapes among the shape  208  of each of the plurality of objects, in order to determine an accurate count  105  of the plurality of objects in the region. In an embodiment, the object counting system  103  may identify one or more overlapping shapes when an intersection is detected among pixel indices corresponding to one or more of the plurality of objects in the image  102 . Further, the object counting system  103  may compute a Mutual Shape Co-occurrence Factor (MSCF) for each pair in the one or more overlapping shapes based on an image gradient identified for each of the one or more overlapping shapes. Finally, one shape among each pair of the one or more overlapping shapes may be eliminated from the image  102  when value of the MSCF, corresponding to the selected shape, is more than a predefined threshold MSCF. 
     For example, consider the image  102  of a plurality of pipes as shown in  FIG. 3G . In an embodiment, the one or more raw detections of shapes corresponding to each of the plurality of pipes in  FIG. 3G  may include one or more overlapping shape detections as shown  FIG. 3H . Consequently, the object counting system  103  may perform detection and elimination of each of the overlapping shape detections using the above method for retaining only non-overlapping shape detections in the image  102 , as shown in  FIG. 3I . Subsequently, the count  105  of the plurality of pipes in the image  102  may be determined by counting each of the non-overlapping shape detections shown in  FIG. 3I . 
       FIG. 4  shows a flowchart illustrating a method of counting plurality of objects placed in a region in accordance with some embodiments of the present disclosure. 
     As illustrated in  FIG. 4 , the method  400  includes one or more blocks illustrating a method of counting plurality of objects placed in a region, using an object counting system  103  for example, the object counting system  103  shown in  FIG. 1 . The method  400  may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform specific functions or implement specific abstract data types. 
     The order in which the method  400  is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the spirit and scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. 
     At block  401 , the method  400  includes partitioning, by the object counting system  103 , an image  102  of the region, comprising the plurality of objects, into one or more segments  206  based on depth of each of the plurality of objects. The image  102  of the region may be received from an image capturing unit  101  associated with the object counting system  103 . In an embodiment, the depth of each of the plurality of objects in each of the one or more segments  206  may be determined using a depth sensor configured in the image capturing unit  101 . 
     At block  403 , the method  400  includes identifying, by the object counting system  103 , one or more object regions  207  in each of the one or more segments  206 . In an embodiment, the one or more object regions  207  may be identified in each of the one or more segments  206  using a pre-trained machine learning classifier configured in the object counting system  103 . 
     At block  405 , the method  400  includes determining, by the object counting system  103 , shape  208  of each of the plurality of objects in each of the one or more object regions  207  of each of the one or more segments  206 . In an embodiment, the shape  208  of each of the plurality of objects may be determined by obtaining spatial frequencies of each of the one or more object regions  207  comprising the plurality of objects, and generating a magnitude spectrum histogram of the spatial frequencies of each of the one or more object regions  207 . Thereafter, a peak value of the magnitude spectrum histogram is associated with a predetermined scaling factor for obtaining pixel coordinates corresponding to the plurality of objects, thereby determining the shape  208  of the plurality of objects. 
     At block  407 , the method  400  includes validating, by the object counting system  103 , the shape  208  of each of the plurality of objects based on comparison of the shape  208  of each of the plurality of objects with predetermined shapes  210 . In an embodiment, a probability score for the shape  208  of each of the plurality of objects may be assigned based on comparison of the shape  208  of each of the plurality of objects with the predetermined shapes  210 . Subsequently, the shape  208  of each of the plurality of objects may be validated when the probability score of the shape  208  is higher than a threshold probability score. 
     At block  409 , the method  400  includes aggregating, by the object counting system  103 , count of the plurality of objects of each shape  208  in each of the one or more segments  206  for determining count  105  of the plurality of objects in the region. In an embodiment, the method  400  may further include eliminating overlap in the plurality of objects identified in the one or more object regions  207  in each of the one or more segments  206  before aggregating the count  105  of the plurality of objects. 
     In an embodiment, pixel indices corresponding to pixel area occupied by each of the plurality of objects may be determined in the one or more object regions  207  for identifying one or more overlapping objects upon when intersection is identified among the pixel indices corresponding to one or more of the plurality of objects. Further, a Mutual Shape Co-occurrence Factor (MSCF) may be computed for each pair in the one or more overlapping objects based on an image gradient identified for each of the one or more overlapping objects. Finally, one object among each pair of the one or more overlapping objects may be selected for eliminating, when value of the MSCF, corresponding to the selected one object, is more than a predefined threshold MSCF. 
     Computer System 
       FIG. 5  illustrates a block diagram of an exemplary computer system  500  for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system  500  may be object counting system  103  shown in  FIG. 1 , which may be used for counting the plurality of objects placed in a region. The computer system  500  may include a central processing unit (“CPU” or “processor”)  502 . The processor  502  may comprise at least one data processor for executing program components for executing user- or system-generated business processes. A user may include a person, a user in the computing environment  100 , or any system/sub-system being operated parallelly to the computer system  500 . The processor  502  may include specialized processing units such as integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. 
     The processor  502  may be disposed in communication with one or more input/output (I/O) devices ( 511  and  512 ) via I/O interface  501 . The I/O interface  501  may employ communication protocols/methods such as, without limitation, audio, analog, digital, stereo, IEEE-1394, serial bus, Universal Serial Bus (USB), infrared, PS/2, BNC, coaxial, component, composite, Digital Visual Interface (DVI), high-definition multimedia interface (HDMI), Radio Frequency (RF) antennas, S-Video, Video Graphics Array (VGA), IEEE 802.n/b/g/n/x, Bluetooth, cellular (e.g., Code-Division Multiple Access (CDMA), High-Speed Packet Access (HSPA+), Global System For Mobile Communications (GSM), Long-Term Evolution (LTE) or the like), etc. Using the I/O interface  501 , the computer system  500  may communicate with one or more I/O devices  511  and  512 . In some implementations, the I/O interface  501  may be used to connect to a user device, such as an image capturing unit  101 , a smartphone, a laptop and the like, associated with the user, through which the object counting system  103  may receive the image  102  of the region. 
     In some embodiments, the processor  502  may be disposed in communication with a communication network  509  via a network interface  503 . The network interface  503  may communicate with the communication network  509 . The network interface  503  may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), Transmission Control Protocol/Internet Protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. Using the network interface  503  and the communication network  509 , the computer system  500  may receive the image  102  from the image capturing unit  101 . Similarly, the computer system  500  may use the communication network  509  for notifying the count  105  of the plurality of objects in the image  102  to the user, through the user device or a user interface associated with the user or the object counting system  103 . 
     In an implementation, the communication network  509  can be implemented as one of the several types of networks, such as intranet or Local Area Network (LAN) and such within the organization. The communication network  509  may either be a dedicated network or a shared network, which represents an association of several types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the communication network  509  may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc. 
     In some embodiments, the processor  502  may be disposed in communication with a memory  505  (e.g., RAM  513 , ROM  514 , etc. as shown in  FIG. 5 ) via a storage interface  504 . The storage interface  504  may connect to memory  505  including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as Serial Advanced Technology Attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fiber channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc. 
     The memory  505  may store a collection of program or database components, including, without limitation, user/application interface  506 , an operating system  507 , a web browser  508 , and the like. In some embodiments, computer system  500  may store user/application data  506 , such as the data, variables, records, etc. as described in this invention. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle® or Sybase®. 
     The operating system  507  may facilitate resource management and operation of the computer system  500 . Examples of operating systems include, without limitation, APPLE® MACINTOSH® OS X®, UNIX®, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTION® (BSD), FREEBSD®, NETBSD®, OPENBSD, etc.), LINUX® DISTRIBUTIONS (E.G., RED HAT®, UBUNTU®, KUBUNTU®, etc.), IBM® OS/2®, MICROSOFT® WINDOWS® (XP®, VISTA®/7/8, 10 etc.), APPLE® IOS®, GOOGLE™ ANDROID™, BLACKBERRY® OS, or the like. 
     The user interface  506  may facilitate display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, the user interface  506  may provide computer interaction interface elements on a display system operatively connected to the computer system  500 , such as cursors, icons, check boxes, menus, scrollers, windows, widgets, and the like. Further, Graphical User Interfaces (GUIs) may be employed, including, without limitation, APPLE® MACINTOSH® operating systems&#39; Aqua®, IBM® OS/2®, MICROSOFT® WINDOWS® (e.g., Aero, Metro, etc.), web interface libraries (e.g., ActiveX®, JAVA®, JAVASCRIPT®, AJAX, HTML, ADOBE® FLASH®, etc.), or the like. 
     The web browser  508  may be a hypertext viewing application. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), and the like. The web browsers  508  may utilize facilities such as AJAX, DHTML, ADOBE® FLASH®, JAVASCRIPT®, JAVA®, Application Programming Interfaces (APIs), and the like. Further, the computer system  500  may implement a mail server stored program component. The mail server may utilize facilities such as ASP, ACTIVEX®, ANSI® C++/C#, MICROSOFT®, .NET, CGI SCRIPTS, JAVA®, JAVASCRIPT®, PERL®, PHP, PYTHON®, WEBOBJECTS®, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), MICROSOFT® exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system  500  may implement a mail client stored program component. The mail client may be a mail viewing application, such as APPLE® MAIL, MICROSOFT® ENTOURAGE®, MICROSOFT® OUTLOOK®, MOZILLA® THUNDERBIRD®, and the like. 
     Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, nonvolatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media. 
     Advantages of the Embodiment of the Present Disclosure are Illustrated Herein 
     In an embodiment, the present disclosure discloses a method for automatically counting plurality of objects placed in a region, and thus drastically reduces the amount of time required for counting the plurality of objects placed in the region. 
     In an embodiment, the method of present disclosure is capable of recognizing and counting the plurality of objects of multiple dimensions and of multiple shape types without the need for human intervention. 
     In an embodiment, the method of present disclosure is capable of effectively recognizing the plurality of objects from an image of the region, even when the image includes disturbances, distortions and/or when the image is captured in shadow/low-lighting conditions. 
     In an embodiment, the method of present disclosure is capable of determining the count of objects which are arranged at different and/or non-uniform depth levels in a region. 
     In an embodiment, the method of present disclosure is capable of identifying and eliminating one or more overlapping object/shape detections while counting the plurality of objects, thereby providing an accurate count of the plurality of objects. 
     The terms “an embodiment”, “embodiment”, “embodiments”, “the embodiment”, “the embodiments”, “one or more embodiments”, “some embodiments”, and “one embodiment” mean “one or more (but not all) embodiments of the invention(s)” unless expressly specified otherwise. 
     The terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless expressly specified otherwise. 
     The enumerated listing of items does not imply that any or all the items are mutually exclusive, unless expressly specified otherwise. The terms “a”, “an” and “the” mean “one or more”, unless expressly specified otherwise. 
     A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention. 
     When a single device or article is described herein, it will be clear that more than one device/article (whether they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether they cooperate), it will be clear that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself. 
     Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims. 
     While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims. 
     REFERRAL NUMERALS 
     
       
         
           
               
               
             
               
                   
               
               
                 Reference Number 
                 Description 
               
               
                   
               
             
            
               
                 100 
                 Environment 
               
               
                 101 
                 Image capturing unit 
               
               
                 102 
                 Image of the region (image) 
               
               
                 103 
                 Object counting system 
               
               
                 105 
                 Count of plurality of objects (count) 
               
               
                 201 
                 I/O interface 
               
               
                 202 
                 Processor 
               
               
                 203 
                 Memory 
               
               
                 204 
                 Data 
               
               
                 205 
                 Modules 
               
               
                 206 
                 One or more segments 
               
               
                 207 
                 One or more object regions 
               
               
                 208 
                 Shape of plurality of objects 
               
               
                 210 
                 Predetermined shapes 
               
               
                 211 
                 Other data 
               
               
                 213 
                 Image partitioning module 
               
               
                 215 
                 Object region identification module 
               
               
                 217 
                 Shape determination module 
               
               
                 219 
                 Shape validation module 
               
               
                 221 
                 Object counting module 
               
               
                 223 
                 Other modules 
               
               
                 500 
                 Exemplary computer system 
               
               
                 501 
                 I/O Interface of the exemplary computer system 
               
               
                 502 
                 Processor of the exemplary computer system 
               
               
                 503 
                 Network interface 
               
               
                 504 
                 Storage interface 
               
               
                 505 
                 Memory of the exemplary computer system 
               
               
                 506 
                 User/Application 
               
               
                 507 
                 Operating system 
               
               
                 508 
                 Web browser 
               
               
                 509 
                 Communication network 
               
               
                 511 
                 Input devices 
               
               
                 512 
                 Output devices 
               
               
                 513 
                 RAM 
               
               
                 514 
                 ROM