Patent Publication Number: US-11037002-B2

Title: Calibration of fixed image-capturing device for depth estimation

Description:
CROSS-RELATED APPLICATIONS 
     This application claims priority of Indian Application Serial No. 201941005110, filed Feb. 8, 2019, the contents of which are incorporated herein by reference. 
     FIELD 
     Various embodiments of the disclosure relate generally to driving assistance systems. More specifically, various embodiments of the disclosure relate to calibration of a fixed image-capturing device for depth estimation. 
     BACKGROUND 
     Advancements in the field of automobiles along with a continuously increasing demand for personal and commercial vehicles have vastly increased the number of vehicles that are plying along various roads on a daily basis, thus increasing the traffic density. The increased traffic density of traffic on the roads has resulted in a rapid rise in the number of collisions of the vehicles with stationary or moving objects. Thus, driving a vehicle is becoming a complex procedure, especially along roads with the dense traffic. While driving through the dense traffic, a driver of the vehicle can encounter critical situations that the driver is unable to solve quickly and thus can cause accident. Hence, to avoid such accidents, it is imperative for the driver to be aware of the possible critical situations well in advance. In one possible solution, the proximity of near-by objects (such as other vehicles, trees, pedestrians, and the like) is estimated and communicated to the driver for providing driving assistance while driving the vehicle. To facilitate providing the driving assistance to the driver, the vehicle is equipped with various proximity sensors that detect presence of the near-by objects and estimate distances of each near-by object from the vehicle. However, the use of proximity sensors has its own limitations. The proximity sensors are typically range-specific and thus are not effective all the time. Further, an angle of coverage of a proximity sensor is typically small (e.g., 5°-15°). Hence, to ensure an entire coverage of the vehicle&#39;s path, multiple proximity sensors are installed on the vehicle, which may not desirable due to increase in cost and complexity. 
     One conventional approach to solve the above-mentioned problems is to use a camera installed on the vehicle to estimate the distances of the near-by objects from the vehicle. This conventional approach estimates the distance of a near-by object from the vehicle based on a ratio between observed dimensions of the object in an image captured by the camera and actual dimensions of the object. However, with such approach, actual dimensions of various objects should be known beforehand, which may not be feasible all the time. 
     In light of the foregoing, there exists a need for a technical and reliable solution that overcomes the above-mentioned problems, challenges, and short-comings, and manages depth estimation of various objects for facilitating driving assistance to drivers of vehicles in a manner that may offer reliable and enhanced experiences to the drivers. 
     SUMMARY 
     Calibration of a fixed image-capturing device for depth estimation is provided substantially as shown in, and described in connection with, at least one of the figures, as set forth more completely in the claims. 
     These and other features and advantages of the disclosure may be appreciated from a review of the following detailed description of the disclosure, along with the accompanying figures in which like reference numerals refer to like parts throughout. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram that illustrates an environment for calibration of fixed image-capturing devices of vehicles for depth estimation, in accordance with an exemplary embodiment of the disclosure; 
         FIG. 2A  illustrates an inside view of a vehicle of the environment of  FIG. 1 , in accordance with an exemplary embodiment of the disclosure; 
         FIG. 2B  illustrates an inside view of the vehicle, in accordance with another exemplary embodiment of the disclosure; 
         FIG. 3A  illustrates a calibration system for calibrating an image-capturing device of the environment of  FIG. 1 , in accordance with an exemplary embodiment of the disclosure; 
         FIG. 3B  illustrates a first image captured by the image-capturing device, in accordance with an exemplary embodiment of the disclosure; 
         FIGS. 4A-4C , collectively, illustrate identification of first and second rows of pixels in the first image of  FIG. 3B  corresponding to first and second calibration lines of  FIG. 3A , respectively, in accordance with an exemplary embodiment of the disclosure; 
         FIG. 5A  illustrates a second image including objects captured by the image-capturing device, in accordance with an exemplary embodiment of the disclosure; 
         FIG. 5B  illustrates predicted distances of the objects from the vehicle, in accordance with an exemplary embodiment of the disclosure; 
         FIGS. 6A and 6B  illustrate a flow chart of a method for predicting distance of an object from the vehicle, in accordance with an exemplary embodiment of the disclosure; 
         FIG. 6C  illustrates a flowchart of a method for identifying the first and second rows of pixels corresponding to the first and second calibration lines, respectively, in accordance with an exemplary embodiment of the disclosure; and 
         FIG. 7  is a block diagram that illustrates a computer system for calibrating a fixed image-capturing device of a vehicle and predicting a distance of an object from the vehicle, in accordance with an exemplary embodiment of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Certain embodiments of the disclosure may be found in a disclosed apparatus for calibration of a fixed image-capturing device of a vehicle for depth estimation. Exemplary aspects of the disclosure provide a method and a system for calibrating the fixed image-capturing device of the vehicle and predicting a distance of an object from the vehicle. The method includes one or more operations that are executed by circuitry of the disclosed apparatus to perform calibration of the fixed image-capturing device. The circuitry may be configured to identify a first plurality of rows of pixels in a first image. The first image may be captured by the image-capturing device. The first image may include a plurality of lines. A first line of the plurality of lines is at a known distance from a second line of the plurality of lines. The first plurality of rows of pixels may correspond to the plurality of lines. The circuitry may be further configured to estimate a first distance and a second distance of the first line and the second line, respectively, from the image-capturing device. The first and second distances are estimated based on at least the known distance, a first width of the first line in the first image, and a second width of the second line in the first image. The circuitry may be further configured to estimate a third distance of a third line corresponding to each row of pixels of a second plurality of rows of pixels in the first image from the image-capturing device. The third distance may be estimated based on at least a focal length of the image-capturing device and a third width of the third line corresponding to each row of pixels. The third width may be estimated based on at least the plurality of lines and each row of pixels. The first image may comprise the first plurality of rows of pixels and the second plurality of rows of pixels. The circuitry may be further configured to store a distance data set including at least the first distance, the second distance, and the third distance in a memory. Further, a fourth distance of a first object from a second object may be predicted based on the stored distance data set and a second image of the first object. 
     Another exemplary aspect of the disclosure provides a method and a system for predicting a distance of an object from a vehicle. The method includes one or more operations that are executed by a vehicle device installed in the vehicle. In one embodiment, the vehicle device may include circuitry such as an image-capturing device, a processor, and a memory. In another embodiment, the vehicle device may only include circuitry such as a processor and a memory, and the image-capturing device and the vehicle device may be separate from each other. In an embodiment, the image-capturing device may be configured to capture a first image of a first object. The processor may be configured to detect a bottom edge of the first object based on the first image. The processor may be further configured to identify a first row of pixels in the first image corresponding to the detected bottom edge. The processor may be further configured to predict a first distance of the first object from the vehicle based on a distance value retrieved from a distance data set that may be stored in the memory of the vehicle device or a separate memory device installed in the vehicle. The distance value may be retrieved from the distance data set based on at least the first row of pixels. 
     The distance data set may be estimated by executing one or more calibration operations. For example, a second plurality of rows of pixels in a second image including a plurality of lines may be identified. The second plurality of rows of pixels may correspond to the plurality of lines. A first line of the plurality of lines may be a known distance from a second line of the plurality of lines. Further, a second distance and a third distance of the first line and second line, respectively, from a second object may be estimated. The second distance and the third distance are estimated based on at least the known distance, a first width of the first line in the second image, and a second width of the second line in the second image. 
     Further, the distance data set for a third line corresponding to each row of pixels of a third plurality of rows of pixels in the second image may be estimated based on at least a third width of the third line corresponding to each of the third plurality of rows of pixels and one of the first line or the second line. Each distance value in the distance data set may indicate a distance of the third line corresponding to each row of pixels of the third plurality of rows of pixels from the second object. The third width may be estimated based on at least the plurality of lines and each row of pixels of the third plurality of rows of pixels. The distance data set may further include at least the second distance and the third distance. The second image may comprise the second plurality of rows of pixels and the third plurality of rows of pixels. The retrieved distance value may be associated with a fourth row of pixels in the second image having a row number that is equal to a row number of the first row of pixels in the first image. Upon prediction of the distance of the first object from the vehicle, the processor may be configured to generate a warning message based on at least the predicted distance. The processor may be further configured to communicate the warning message to a driver of the vehicle indicating an impending collision. 
     Thus, various methods and systems of the disclosure facilitate calibration of a fixed image-capturing device of a vehicle to obtain distance data set that is further utilized to predict a distance of a first object from the vehicle in real-time. The distance data set may be obtained by identifying a plurality of rows of pixels and determining corresponding distances by utilizing line correspondences of a plurality of lines on a ground plane. The plurality of lines may be mapped to a plurality of rows of pixels in an image captured by the fixed image-capturing device during the calibration of the fixed image-capturing device. Thus, the accuracy of identification of the plurality rows of pixels and determination of the distance data set may be higher as compared to conventional distance prediction approaches that use point correspondences. Further, since distances of the distance data set may be estimated based on the plurality of lines on the ground plane, and distances of various objects (such as the first object) from the vehicle may be predicted based on the distance data set, a beforehand need for knowing actual dimensions of the various objects is eliminated. Thus, the various methods and systems of the disclosure facilitate an efficient, effective, and accurate way of identifying various obstacles, predicting distances of each obstacle, and notifying a driver of the vehicle of an impending collision, if any, well in advance. 
       FIG. 1  is a block diagram that illustrates an environment  100  for calibration of fixed image-capturing devices of vehicles for depth estimation, in accordance with an exemplary embodiment of the disclosure. The environment  100  includes a vehicle  102 , a vehicle device  104  including an image-capturing device  106 , a processor  108 , and a memory  110 , an image-capturing device  112 , a vehicle device  114  including a processor  116  and a memory  118 , an application server  120 , and a database server  122  that communicate with each other via a communication network  124 . 
     In an embodiment, the vehicle  102  may include a distance prediction mechanism. The distance prediction mechanism may facilitate detection of a first object in front of the vehicle  102  and prediction of a first distance of the first object from the vehicle  102 . Examples of the first object may include, but are not limited to, a pedestrian, an animal, a vehicle, a road-divider, a non-drivable area, a rock, a road sign, a building, and a tree. For facilitating prediction of the first distance in real-time, the distance prediction mechanism may be calibrated. Thus, the vehicle  102  may be subjected to a calibration phase (in which the distance prediction mechanism may be calibrated) and an implementation phase (in which the distance prediction mechanism may predict the first distance based on the calibration). In an embodiment, the distance prediction mechanism may be realized or implemented by the vehicle device  104  including the image-capturing device  106 , the processor  108 , and the memory  110 . In another embodiment, the distance prediction mechanism may be realized or implemented by a combination of the image-capturing device  112  and the vehicle device  114  including the processor  116  and the memory  118 . Here, the image-capturing device  112  and the vehicle device  114  are separate devices installed in the vehicle  102 . In another embodiment, the distance prediction mechanism may be realized or implemented by a combination of the image-capturing device  106  or  112 , the application server  120 , and the database server  122 . 
     The vehicle  102  is a mode of transportation that is used by a user to commute from one location to another location. In an embodiment, the vehicle  102  may be owned by the user. In another embodiment, the vehicle  102  may be owned by a vehicle service provider for offering on-demand vehicle or ride services to one or more passengers (e.g., the user). The vehicle  102  may include one or more vehicle devices such as the vehicle device  104  or the vehicle device  114 . Examples of the vehicle  102  include, but are not limited to, an automobile, a bus, a car, an auto-rickshaw, and a bike. 
     The vehicle device  104  may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to perform one or more operations. The vehicle device  104  may be a computing device that is installed in the vehicle  102 . Examples of the vehicle device  104  may include, but are not limited to, a mobile phone, a tablet, a laptop, a vehicle head unit, or any other portable communication device that is placed inside the vehicle  102 . The vehicle device  104  may be realized through various web-based technologies such as, but not limited to, a Java web-framework, a .NET framework, a PHP (Hypertext Preprocessor) framework, or any other web-application framework. The vehicle device  104  may be further realized through various embedded technologies such as, but not limited to, microcontrollers or microprocessors that are operating on one or more operating systems such as Windows, Android, Unix, Ubuntu, Mac OS, or the like. 
     In an embodiment, the vehicle device  104  may be installed on a front windshield (shown in  FIGS. 2A and 3A ) of the vehicle  102 . Further, the vehicle device  104  may be installed such that the image-capturing device  106  is positioned at a center of the front windshield and may be oriented to face outside the vehicle  102  for capturing various objects (such as the first object) present in front of the vehicle  102 . In another embodiment, the vehicle device  104  may be installed behind the front windshield of the vehicle  102 . Further, the vehicle device  104  may be installed such that the image-capturing device  106  is positioned at a center of the front windshield and may be oriented to face outside the vehicle  102  for capturing the various objects (such as the first object) present in front of the vehicle  102 . In another embodiment, the vehicle device  104  may be installed on a rear windshield (not shown) of the vehicle  102 . Further, the vehicle device  104  may be installed such that the image-capturing device  106  is positioned at a center of the rear windshield and may be oriented to face outside the vehicle  102  for capturing the various objects present behind the vehicle  102 . 
     In an embodiment, the vehicle device  104  may be calibrated in the calibration phase. Based on the calibration, the vehicle device  104  may be configured to predict the first distance of the first object from the vehicle  102  in the implementation phase. The vehicle device  104  may be configured to execute the calibration and implementation phases by utilizing the image-capturing device  106 , the processor  108 , and the memory  110 . The image-capturing device  106 , the processor  108 , and the memory  110  may communicate with each other via a first communication bus (not shown). 
     The image-capturing device  106  may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to perform one or more operations. For example, the image-capturing device  106  may be an optical instrument that includes one or more image sensors for recording or capturing a set of images. The set of images may be individual still photographs or a sequence of images constituting a video. In an embodiment, the image-capturing device  106  may be a red, green, and blue (RGB) camera. Thus, the set of images (captured by the image-capturing device  106 ) may be associated with an RGB color model. However, a person having ordinary skill in the art would understand that the scope of the disclosure is not limited to this specific scenario in which the image-capturing device  106  may be configured to capture the set of images associated with the RGB color model. In various other embodiments, the set of images captured by the image-capturing device  106  may be associated with a different color model such as a black and white color model, a greyscale color model, a hue, saturation, and value (HSV) color model, a cyan, magenta, yellow, and black (CMYK) color model, a hue, saturation, and brightness (HSB) color model, a hue, saturation, and lightness (HSL) color model, a hue, chroma, and value (HCV) color model, or the like. 
     In an embodiment, in the calibration phase, the image-capturing device  106  may be configured to capture a first image (shown in  FIGS. 3B and 4A ) of a calibration system or environment (shown in  FIG. 3A ). In one example, the image-capturing device  106  may automatically capture the first image. In another example, the image-capturing device  106  may capture the first image of the calibration system based on an input triggered by an individual such as an administrator who is monitoring and managing the calibration phase. 
     In an embodiment, the calibration system or environment (hereinafter, “the calibration system”) may include the vehicle  102 , a carpet (shown in  FIGS. 3A and 4A ), and first and second calibration lines (shown in  FIGS. 3A, 3B, and 4A ) painted on the carpet. The first and second calibration lines may be perpendicular to a path of the vehicle  102 . In another embodiment, the calibration system may include the vehicle  102  and third and fourth calibration lines (not shown) drawn on a ground plane (not shown) and the third and fourth calibration lines may be perpendicular to the path of the vehicle  102 . In another embodiment, the calibration system may include the vehicle  102  and first and second colored tapes (not shown) pasted on the ground plane and the first and second colored tapes may be perpendicular to the path of the vehicle  102 . In another embodiment, the calibration system may include the vehicle  102  and an enclosed space (not shown) with first and second rows of lights (not shown). The first and second rows of lights may be perpendicular to the path of the vehicle  102 . The first and second calibration lines may be at a known distance from each other. Similarly, the third and fourth calibration lines may be at the known distance from each other. Further, the first and second colored tapes may be at the known distance from each other, and the first and second rows of lights may be at the known distance from each other. Further, the first and second calibration lines, the third and fourth calibration lines, the first and second colored tapes, and the first and second rows of lights may be associated with known widths. 
     For the sake of ongoing description, it is assumed that the calibration system includes the carpet laid in front of the vehicle  102  with the first and second calibration lines painted on the carpet. It is further assumed that the first and second calibration lines are having equal known widths (for example, 2 meters (m)) and are at the known distance from each other. It will be apparent to a person skilled in the art that the scope of the disclosure is not limited to the calibration system including only two calibration lines (such as the first and second calibration lines, the third and fourth calibration lines, the first and second colored tapes, or the first and second rows of lights). In various other embodiments, the calibration phase may be realized by using more than two calibration lines. Thus, the first image of the calibration system may include at least the carpet along with the first and second calibration lines painted on the carpet. Upon capturing of the first image of the calibration system, the image-capturing device  106  may be configured to transmit the first image (e.g., image data associated with the first image) to the processor  108 , store the first image in the memory  110 , transmit the first image to the application server  120 , or transmit the first image to the database server  122 . 
     In an embodiment, in the implementation phase, the image-capturing device  106  may be configured to capture a second image (shown in  FIGS. 5A and 5B ) of a road (or a route segment) along which the vehicle  102  is currently traversing. Upon capturing of the second image, the image-capturing device  106  may be configured to transmit the second image (e.g., image data associated with the second image) to the processor  108 , store the second image in the memory  110 , transmit the second image to the application server  120 , or transmit the second image to the database server  122 . The second image may include one or more objects (such as the first object) or a portion of the one or more objects that are present in a capturing range of the image-capturing device  106 . In some embodiments, the second image may include various other objects apart from the first object. Examples of such objects include, but are not limited to, pedestrians, animals, other vehicles, road-dividers, non-drivable areas, rocks, road signs, buildings, and trees. For the sake of ongoing description, it is assumed that the distance prediction mechanism predicts the first distance of the first object from the vehicle  102 . In some embodiments, the distance prediction mechanism may also predict distances of various other objects from the vehicle  102  in a manner similar to the prediction of the first distance of the first object from the vehicle  102 . 
     The processor  108  may include suitable logic, circuitry, interfaces, and/or codes, executable by the circuitry, that may be configured to perform one or more operations. Examples of the processor  108  may include, but are not limited to, an application-specific integrated circuit (ASIC) processor, a reduced instruction set computing (RISC) processor, a complex instruction set computing (CISC) processor, and a field-programmable gate array (FPGA). It will be apparent to a person skilled in the art that the processor  108  may be compatible with multiple operating systems. 
     In an embodiment, the one or more operations may be associated with the calibration phase and the implementation phase. In an embodiment, in the calibration phase, the processor  108  may be configured to receive the first image from the image-capturing device  106 . The processor  108  may be further configured to process the first image to identify first and second rows of pixels (shown in  FIG. 3B ) in the first image. The first and second rows of pixels (i.e., a first plurality of rows of pixels) may correspond to the first and second calibration lines painted on the carpet, respectively. The processor  108  may be further configured to estimate a second distance and a third distance of the first calibration line and the second calibration line, respectively, from the vehicle  102  (or the image-capturing device  106  of the vehicle  102 ). The processor  108  may estimate the second and third distances based on the known distance between the first and second calibration lines, a width of the first calibration line as observed in the first image (hereinafter, “a first observed width”), and a width of the second calibration line as observed in the first image (hereinafter, “a second observed width”). The processor  108  may be further configured to estimate a focal length of the image-capturing device  106  based on at least the first calibration line or the second calibration line. In an example, the processor  108  may estimate the focal length of the image-capturing device  106  based on the second distance of the first calibration line from the vehicle  102  (or the image-capturing device  106  of the vehicle  102 ), the known width (e.g., 2 m) of the first calibration line on the carpet, and the first observed width. In another example, the processor  108  may estimate the focal length of the image-capturing device  106  based on the third distance of the second calibration line from the vehicle  102  (or the image-capturing device  106  of the vehicle  102 ), the known width (e.g., 2 m) of the second calibration line on the carpet, and the second observed width. The known distance and the known widths are shown in  FIG. 3A , and the first and second observed widths are shown in  FIG. 3B . 
     In an embodiment, the processor  108  may be further configured to estimate a width of each line corresponding to each of remaining rows of pixels (i.e., a second plurality of rows of pixels) in the first image. For example, for each remaining row of pixels in the first image, the processor  108  may be configured to determine the width of a corresponding line based on a fourth distance between the corresponding row of pixels and the first or second row of pixels, the first and second observed widths, and a first angle. The first angle may be obtained by joining the same ends of the first and second calibration lines (as shown in  FIG. 3B ). The remaining rows of pixels in the first image may correspond to the second plurality of rows of pixels in the first image that does not include the first plurality of rows of pixels (i.e., the first and second rows of pixels). The processor  108  may be further configured to estimate a fifth distance of a line corresponding to each remaining row of pixels in the first image from the vehicle  102  (or the image-capturing device  106  of the vehicle  102 ) based on at least the estimated width of the corresponding line, the known width, and the focal length of the image-capturing device  106 . Thus, each row of pixels in the first image may be associated with a corresponding distance from the vehicle  102  (or the image-capturing device  106  of the vehicle  102 ). In an embodiment, the processor  108  may be further configured to store the second and third distances of the first and second calibration lines along with the estimated fifth distances associated with the remaining rows of pixels in the memory  110 . The second and third distances and the estimated fifth distances may be collectively referred to as, “a distance data set” that is obtained from the calibration phase. In another embodiment, the processor  108  may be further configured to store the distance data set in the database server  122 . 
     In an embodiment, in the implementation phase, the processor  108  may be configured to receive the second image from the image-capturing device  106 . The processor  108  may be further configured to process the second image to identify the one or more objects (such as the first object) captured in the second image. Further, the processor  108  may be configured to detect a first bottom edge of the first object. The processor  108  may be further configured to identify, in the second image, a third row of pixels associated with the first bottom edge. In an embodiment, based on the third row of pixels, the processor  108  may be further configured to retrieve a first distance value from the distance data set stored in the memory  110 . In another embodiment, based on the third row of pixels, the processor  108  may be configured to transmit a query to the database server  122  to retrieve the first distance value from the distance data set stored in the database server  122 . The first distance value may correspond to a fourth row of pixels in the first image having a row number that is equal to a row number of the third row of pixels in the second image. Thereafter, the processor  108  may be configured to predict the first distance of the first object from the vehicle  102  based on at least the retrieved first distance value. Based on the predicted first distance, the processor  108  may be configured to generate a warning message and communicate the warning message to a driver of the vehicle  102  to provide driving assistance in real-time. For example, if the first distance is less than a threshold value, the processor  108  may generate the warning message indicating an impending collision, for example, the generated warning message may read as “Hey driver! There is an obstacle at 5 m from your vehicle. The chances of the impending collision with the obstacle is high if you drive at the current speed. Go slow!!”. Further, the processor  108  may communicate the warning message to the driver. The warning message may be communicated to the driver by communicating a short message service (SMS), an audio message, a video message, a haptic message, or the like. 
     The memory  110  may include suitable logic, circuitry, interfaces, and/or codes, executable by the circuitry, that may be configured to store one or more instructions that are executed by the image-capturing device  106  or the processor  108  to perform their operations. The memory  110  may be configured to store the first image, the second image, and the distance data set. Examples of the memory  110  may include, but are not limited to, a random-access memory (RAM), a read-only memory (ROM), a programmable ROM (PROM), and an erasable PROM (EPROM). 
     The image-capturing device  112  may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to perform one or more operations. For example, the image-capturing device  112  may be an optical instrument that includes one or more image sensors for recording or capturing a set of images. The set of images may be individual still photographs or a sequence of images constituting a video. In an embodiment, the image-capturing device  112  may be installed on the front windshield of the vehicle  102  such that the image-capturing device  112  is positioned at a center of the front windshield and may be oriented to face outside the vehicle  102  for capturing the various objects (such as the first object) present in front of the vehicle  102 . In another embodiment, the image-capturing device  112  may be installed behind the front windshield such that the image-capturing device  112  is positioned at a center of the front windshield and may be oriented to face outside the vehicle  102  for capturing the various objects present in front of the vehicle  102 . In another embodiment, the image-capturing device  112  may be installed on the rear windshield of the vehicle  102  such that the image-capturing device  112  is positioned at a center of the rear windshield and may be oriented to face outside the vehicle  102  for capturing the various objects present behind the vehicle  102 . The image-capturing device  112  may be connected to the vehicle device  114  via the communication network  124  or a second communication bus (not shown). Further, the image-capturing device  112  may be connected to the application server  120  via the communication network  124 . 
     In an embodiment, in the calibration phase, the image-capturing device  112  may be configured to capture the first image of the calibration system and transmit the first image to the vehicle device  114  or the application server  120 . In an embodiment, in the implementation phase, the image-capturing device  112  may be configured to capture the second image of the road (or the route segment) along which the vehicle  102  is currently traversing. Upon capturing of the second image, the image-capturing device  112  may be configured to transmit the second image to the processor  116 , store the second image in the memory  118 , transmit the second image to the application server  120 , or transmit the second image to the database server  122 . The second image may include the one or more objects (such as the first object) or a portion of the one or more objects that are present in a capturing range of the image-capturing device  112 . The image-capturing device  112  may be structurally and functionally similar to the image-capturing device  106 . However, the image-capturing device  106  is embedded within the vehicle device  104  whereas the image-capturing device  112  is a stand-alone device. 
     The vehicle device  114  may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to perform one or more operations. The vehicle device  114  may be a computing device that is installed in the vehicle  102 . Examples of the vehicle device  114  may include, but are not limited to, a mobile phone, a tablet, a laptop, a vehicle head unit, or any other portable communication device that is placed inside the vehicle  102 . The vehicle device  114  may be realized through various web-based technologies such as, but not limited to, a Java web-framework, a .NET framework, a PHP framework, or any other web-application framework. The vehicle device  114  may be further realized through various embedded technologies such as, but are not limited to, microcontrollers or microprocessors that are operating on one or more operating systems such as Windows, Android, Unix, Ubuntu, Mac OS, or the like. 
     In an embodiment, the vehicle device  114  may be communicatively connected to the image-capturing device  112  for receiving one or more images (such as the first image and the second image) captured by the image-capturing device  112 . The vehicle device  114  may be configured to execute various processes of the calibration and implementation phases by utilizing the processor  116  and the memory  118 . In an embodiment, the processor  116  and the memory  118  may communicate with each other via a third communication bus (not shown). 
     The processor  116  may include suitable logic, circuitry, interfaces, and/or codes, executable by the circuitry, that may be configured to perform one or more operations. Examples of the processor  116  may include, but are not limited to, an ASIC processor, a RISC processor, a CISC processor, and an FPGA. It will be apparent to a person skilled in the art that the processor  116  may be compatible with multiple operating systems. 
     In an embodiment, the one or more operations may be associated with the calibration phase and the implementation phase. In an embodiment, in the calibration phase, the processor  116  may be configured to receive the first image from the image-capturing device  112 . The processor  116  may be further configured to process the first image to identify the first and second rows of pixels in the first image. The first and second rows of pixels (i.e., the first plurality of rows of pixels) may correspond to the first and second calibration lines. The processor  116  may be further configured to estimate a second distance and a third distance of the first calibration line and the second calibration line, respectively, from the vehicle  102  (or the image-capturing device  106  of the vehicle  102 ). The processor  116  may estimate the second and third distances based on the known distance between the first and second calibration lines, the first observed width, and the second observed width. Based on at least the first calibration line or the second calibration line, the processor  116  may be further configured to estimate the focal length of the image-capturing device  112 . In an example, the processor  116  may estimate the focal length of the image-capturing device  112  based on the second distance of the first calibration line from the vehicle  102  (or the image-capturing device  112  of the vehicle  102 ), the known width of the first calibration line on the carpet, and the first observed width. In another example, the processor  116  may estimate the focal length of the image-capturing device  112  based on the third distance of the second calibration line from the vehicle  102  (or the image-capturing device  112  of the vehicle  102 ), the known width of the second calibration line on the carpet, and the second observed width. 
     In an embodiment, the processor  116  may be further configured to estimate the width of each line corresponding to each of remaining rows of pixels (i.e., the second plurality of rows of pixels) in the first image. For example, for each remaining row of pixels in the first image, the processor  116  may determine the width of the corresponding line based on the fourth distance, the first and second observed widths, and the first angle. The processor  116  may be further configured to estimate the fifth distance of a line corresponding to each remaining row of pixels in the first image from the vehicle  102  (or the image-capturing device  112  of the vehicle  102 ) based on at least the estimated width of the corresponding line, the known width, and the focal length of the image-capturing device  112 . Thus, each row of pixels in the first image may be associated with a corresponding distance from the vehicle  102  (or the image-capturing device  112  of the vehicle  102 ). In an embodiment, the processor  116  may be further configured to store the distance data set (i.e., the second and third distances and the estimated fifth distances) in the memory  118 . In another embodiment, the processor  116  may be further configured to store the distance data set in the database server  122 . 
     In an embodiment, in the implementation phase, the processor  116  may be configured to receive the second image from the image-capturing device  112 . The processor  116  may be further configured to process the second image to identify the one or more objects (such as the first object) captured in the second image. Further, the processor  116  may be configured to detect the first bottom edge of the first object. The processor  116  may be further configured to identify, in the second image, the third row of pixels associated with the first bottom edge. In an embodiment, based on the third row of pixels, the processor  116  may be further configured to retrieve the first distance value from the distance data set stored in the memory  118 . In another embodiment, based on the third row of pixels, the processor  116  may be further configured to transmit a query to the database server  122  to retrieve the first distance value from the distance data set stored in the database server  122 . The first distance value may correspond to the fourth row of pixels in the first image having a row number that is equal to a row number of the third row of pixels in the second image. Thereafter, the processor  116  may be configured to predict the first distance of the first object from the vehicle  102  based on at least the retrieved first distance value. Based on the predicted first distance, the processor  116  may be configured to generate the warning message and communicate the warning message to the driver of the vehicle  102  to provide driving assistance in real-time. Additionally, the processor  116  may generate the warning message based on the predicted first distance and speed of the vehicle  102 . 
     The memory  118  may include suitable logic, circuitry, interfaces, and/or codes, executable by the circuitry, that may be configured to store one or more instructions that are executed by the processor  116  to perform the one or more operations. The memory  118  may be configured to store the first image, the second image, and the distance data set. Examples of the memory  118  may include, but are not limited to, a RAM, a ROM, a PROM, and an EPROM. 
     The application server  120  may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to perform one or more operations for device calibration and depth estimation. The application server  120  may be a computing device, which may include a software framework, that may be configured to create the application server implementation and perform the various operations associated with the device calibration and depth estimation. The application server  120  may be realized through various web-based technologies, such as, but not limited to, a Java web-framework, a .NET framework, a PHP framework, a python framework, or any other web-application framework. Examples of the application server  120  may include, but are not limited to, a personal computer, a laptop, or a network of computer systems. The application server  120  may be communicatively connected to the vehicle device  104  or the image-capturing device  112  and vehicle device  114  via the communication network  124 . 
     In an exemplary embodiment, the application server  120  may be configured to receive the first image from the image-capturing device  112 . The application server  120  may be further configured to process the first image to identify the first and second rows of pixels in the first image corresponding to the first and second calibration lines, respectively. The application server  120  may be further configured to estimate the second distance and the third distance of the first calibration line and the second calibration line, respectively, from the vehicle  102  (or the image-capturing device  112  of the vehicle  102 ). The application server  120  may estimate the second and third distances based on the known distance between the first and second calibration lines, the first observed width, and the second observed width. Based on at least the first calibration line or the second calibration line, the application server  120  may be further configured to estimate the focal length of the image-capturing device  112 . The application server  120  may be further configured to estimate, for each remaining row of pixels in the first image, the width of the corresponding line based on the fourth distance, the first and second observed widths, and the first angle. The application server  120  may be further configured to estimate the fifth distance of a line corresponding to each remaining row of pixels from the vehicle  102  (or the image-capturing device  112  of the vehicle  102 ) based on the estimated width of the corresponding line, the known width, and the focal length. Thus, each row of pixels in the first image may be associated with a corresponding distance from the vehicle  102  (or the image-capturing device  112  of the vehicle  102 ). In an embodiment, the application server  120  may be further configured to store the distance data set (i.e., the second and third distances and the estimated fifth distances) in the database server  122 . 
     In an embodiment, in the implementation phase, the application server  120  may be configured to receive the second image from the image-capturing device  112 . The application server  120  may be further configured to process the second image to identify the one or more objects (such as the first object) captured in the second image. Further, the application server  120  may be configured to detect the first bottom edge of the first object and identify the third row of pixels associated with the first bottom edge in the second image. Based on the third row of pixels, the application server  120  may be further configured to transmit a query to the database server  122  to retrieve the first distance value from the distance data set stored in the database server  122 . The first distance value may correspond to the fourth row of pixels in the first image having a row number that is equal to a row number of the third row of pixels in the second image. Thereafter, the application server  120  may be configured to predict the first distance of the first object from the vehicle  102  based on at least the retrieved first distance value. Based on the predicted first distance, the application server  120  may be further configured to generate the warning message and communicate the warning message to the driver of the vehicle  102  to provide driving assistance in real-time. 
     The database server  122  may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to perform one or more operations, such as receiving, storing, processing, and transmitting queries, data, or content. The database server  122  may be a data management and storage computing device that is communicatively coupled to the vehicle device  104 , the image-capturing device  112 , the vehicle device  114 , and the application server  120  via the communication network  124  to perform the one or more operations. In an exemplary embodiment, the database server  122  may be configured to manage and store one or more images (such as the first image) captured by the image-capturing device  106  or  112 . The database server  122  may be further configured to manage and store one or more images (such as the second image) captured by the image-capturing device  106  or  112 . The database server  122  may be further configured to manage and store the distance data set. The database server  122  may be further configured to manage and store one or more warning messages corresponding to one or more first distances of the one or more objects from the vehicle  102 . 
     In an embodiment, the database server  122  may be configured to receive one or more queries from the processor  108 , the processor  116 , or the application server  120  via the communication network  124 . Each query may correspond to an encrypted message that is decoded by the database server  122  to determine a request for retrieving requisite information. In response to each received query, the database server  122  may be configured to retrieve and communicate the requested information to the processor  108 , the processor  116 , or the application server  120  via the communication network  124 . Examples of the database server  122  include, but are not limited to, a personal computer, a laptop, or a network of computer systems. 
     The communication network  124  may include suitable logic, circuitry, interfaces, and/or code, executable by the circuitry, that may be configured to transmit queries, data, content, messages, and requests between various entities, such as the vehicle device  104 , the image-capturing device  112 , the vehicle device  114 , the application server  120 , and/or the database server  122 . Examples of the communication network  124  include, but are not limited to, a wireless fidelity (Wi-Fi) network, a light fidelity (Li-Fi) network, a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a satellite network, the Internet, a fiber optic network, a coaxial cable network, an infrared (IR) network, a radio frequency (RF) network, and a combination thereof. Various entities in the environment  100  may connect to the communication network  124  in accordance with various wired and wireless communication protocols, such as Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Long Term Evolution (LTE) communication protocols, or any combination thereof. 
     Although the present disclosure describes the calibration and implementation phases being executed in association with the same vehicle (e.g., the vehicle  102 ), it will be apparent to a person skilled in the art that the scope of the disclosure is not limited to the same vehicle for executing the calibration and implementation phases. In various other embodiments, the calibration and implementation phases may be executed for two separate vehicles (such as a first vehicle and a second vehicle). In such a scenario, to ensure accuracy of the distance prediction, a position of an image-capturing device (such as the image-capturing device  106  or the image-capturing device  112 ) on the first vehicle in the calibration phase is the same as a position of another image-capturing device on the second vehicle in the implementation phase. Further, specification of the image-capturing devices used with the first vehicle and the second vehicle may be the same. Further, various dimensions of the first vehicle and the second vehicle may be similar. Various operations associated with the calibration of fixed image-capturing devices of vehicles for depth estimation have been described in detail in conjunction with  FIGS. 2A, 2B, 3A, and 3B . 
       FIG. 2A  illustrates an inside view of the vehicle  102 , in accordance with an exemplary embodiment of the disclosure. The vehicle  102  may include the vehicle device  104  that is installed on a front windshield  200  of the vehicle  102 . The vehicle device  104  may include the image-capturing device  106 , the processor  108 , and the memory  110  that are communicatively connected to each other via the first communication bus. Also, the vehicle device  104  may be communicatively connected to the application server  120  via the communication network  124 . In an embodiment, the image-capturing device  106  is positioned perpendicular to the ground plane to ensure that sizes and distances of the various objects (such as the first object) captured by the image-capturing device  106  are not distorted. 
     It will be apparent to a person skilled in the art that the scope of the disclosure is not limited to the installation of the vehicle device  104  as shown in  FIG. 2A . In various other embodiments, the position of the vehicle device  104  in the vehicle  102  may vary. However, the position of the image-capturing device  106  in the vehicle  102  may remain intact with respect to the calibration phase and the implementation phase. For example, if the position of the image-capturing device  106  installed with the vehicle  102  is defined by x 1 , y 1 , and z 1  coordinates in the calibration phase, then the position of the image-capturing device  106  installed with the vehicle  102  may be defined by the same x 1 , y 1 , and z 1  coordinates in the implementation phase. The x 1 , y 1 , and z 1  coordinates may be defined with respect to a fixed point on the vehicle  102 . 
       FIG. 2B  illustrates an inside view of the vehicle  102 , in accordance with another exemplary embodiment of the disclosure. The vehicle  102  may include the image-capturing device  112  that is installed on the front windshield  200  of the vehicle  102 . The vehicle  102  may further include the vehicle device  114  that is installed on a dashboard  202  of the vehicle  102 . The vehicle device  114  may include the processor  116  and the memory  118  that are communicatively connected to each other via the third communication bus. The image-capturing device  112  may be communicatively connected to the vehicle device  114  via the communication network  124  or the second communication bus. Also, the image-capturing device  112  and the vehicle device  114  may be communicatively connected to the application server  120  via the communication network  124 . In an embodiment, the image-capturing device  112  is positioned perpendicular to the ground plane to ensure that sizes and distances of the various objects captured by the image-capturing device  112  are not distorted. 
     It will be apparent to a person skilled in the art that the scope of the disclosure is not limited to the installation of the image-capturing device  112  and the vehicle device  114  as shown in  FIG. 2B . In various other embodiments, the positions of the image-capturing device  112  and the vehicle device  114  in the vehicle  102  may vary. However, the position of the image-capturing device  112  in the vehicle  102  may remain intact with respect to the calibration phase and the implementation phase. For example, if the position of the image-capturing device  112  installed with the vehicle  102  is defined by x 2 , y 2 , and z 2  coordinates in the calibration phase, then the position of the image-capturing device  112  installed with the vehicle  102  may be defined by the same x 2 , y 2 , and z 2  coordinates in the implementation phase. The x 2 , y 2 , and z 2  coordinates may be defined with respect to a fixed point on the vehicle  102 . 
       FIG. 3A  illustrates the calibration system  300  for calibrating the image-capturing device  106  of the vehicle  102 , in accordance with an exemplary embodiment of the disclosure. The calibration system  300  may include the vehicle  102  and the carpet  302  having the first and second calibration lines c 1  and c 2 . The first and second calibration lines c 1  and c 2  may be associated with the same known width W and may be at the known distance d from each other. In an embodiment, the second distance of the first calibration line c 1  from the vehicle  102  is illustrated in the  FIG. 3A  as “x” (hereafter referred to as, “the second distance x”). Further, the third distance of the second calibration line c 2  from the vehicle  102  is illustrated in the  FIG. 3B  as “y” (hereafter referred to as, “the third distance y”), where y=d+x. Further, the first and second calibration lines c 1  and c 2  may be parallel to each other and perpendicular to the path of the vehicle  102 . In an embodiment, the vehicle device  104  may be installed on the front windshield  200  of the vehicle  102  such that the image-capturing device  106  may be at the center of the front windshield  200 . In another embodiment, the image-capturing device  112  may be installed at the center of the front windshield  200  for capturing the first image of the calibration system  300 . For the sake of ongoing description, it is assumed that the vehicle device  104  is installed on the front windshield  200 . Further, the vehicle  102  is moving on the carpet  302 , and the image-capturing device  106  may be configured to capture the first image of the calibration system  300 . 
       FIG. 3B  illustrates the first image  304  captured by the image-capturing device  106 , in accordance with an exemplary embodiment of the disclosure. The first image  304  may include the first and second calibration lines c 1  and c 2 . In an exemplary embodiment, one or more points on the carpet  302  (that lie at a distance from the vehicle  102 ) may be observed at the same distance (i.e., height) in the first image  304 . In other words, the one or more points associated with the first calibration line c 1  painted on the carpet  302  at the second distance x from the vehicle  102  may be observed in the first image  304  along the same row of pixels (i.e., the first row of pixels r 1 ). Similarly, the one or more points associated with the second calibration line c 2  painted on the carpet  302  at the third distance y from the vehicle  102  may be observed in the first image  304  along the same row of pixels (i.e., the second row of pixels r 2 ). Thus, the first and second rows of pixels r 1  and r 2  may be at the second and third distances x and y from the vehicle  102 , respectively. 
     As illustrated in  FIG. 3A , the second distance x is less than the third distance y. Hence, the first observed width w 1  (i.e., the width of the first calibration line c 1  as observed in the first image  304 ) is greater than the second observed width w 2  (i.e., the width of the second calibration line c 2  as observed in the first image  304 ) even though the first and second calibration lines c 1  and c 2  may have the same width (i.e., the known width W) on the carpet  302 . The difference in the first and second observed widths w 1  and w 2  may be represented by δ w . Further, when the first and second calibration lines c 1  and c 2  are centered with respect to an axis passing through the center of the image-capturing device  106 , the difference in the first and second observed widths w 1  and w 2  may be symmetric on both sides of the axis (illustrated as δ w /2 in the first image  304  of  FIG. 3B ). 
     It will be apparent to a person having ordinary skill in the art that vertical edges of the carpet  302  (that are straight lines in the calibration system  300 ) may be observed as tilted lines in the first image  304  due to perspective distortion. In other words, the carpet  302  with a rectangular shape may be observed as a trapezoid in the first image  304 . The distance between the first and second rows of pixels (illustrated by h in the first image  304 ) may correspond to the height of the trapezoid. An angle θ may be the first angle between the first calibration line c 1  and a first line joining the end points of the first and second calibration lines c 1  and c 2 , as shown in  FIG. 3B . Similarly, an angle α may be a second angle between the first calibration line c 1  and a second line joining the end points of the first and second calibration lines c 1  and c 2 . The first and second lines may correspond to two non-parallel sides of the trapezoid and the angles θ and α may correspond to base angles of the trapezoid. When the first and second calibration lines c 1  and c 2  are centered with respect to the axis of the image-capturing device  106 , lengths of the first and second lines may be equal and the angle θ may be equal to the angle α. In other words, the carpet  302  may be observed as an isosceles trapezoid in the first image  304 . 
     In operation, the distance prediction mechanism associated with the vehicle  102  may be calibrated in the calibration phase to predict the first distance of the first object captured in front of the vehicle  102  in the implementation phase. For the sake of ongoing description, it is assumed that the distance prediction mechanism may be realized and implemented by utilizing the vehicle device  104  that is installed with the vehicle  102 . The vehicle device  104  may include the image-capturing device  106 , the processor  108 , and the memory  110 . However, it will be apparent to a person skilled in the art that the scope of the disclosure is not limited to the realization and implementation of the distance prediction mechanism by utilizing the vehicle device  104 . In various other embodiments, the distance prediction mechanism may be realized and implemented by utilizing the image-capturing device  112  and the vehicle device  114 , the image-capturing device  112 , the application server  120 , and the database server  122 , or any combination thereof. 
     In the calibration phase, the vehicle device  104  may be calibrated by utilizing the calibration system  300 . The calibration system  300  may include the first and second calibration lines c 1  and c 2  on the carpet  302  such that the first and second calibration lines c 1  and c 2  may have the same width (i.e., the known width W) that is equal to a width of the carpet  302 . Further, the first calibration line c 1  may be drawn at the known distance d from the second calibration line c 2 . The first and second calibration lines c 1  and c 2  may be parallel to each other and perpendicular to the path of the vehicle  102 . In an embodiment, during execution of calibration processes of the calibration phase, the vehicle  102  is traversing along the carpet  302 , and the image-capturing device  106  may be configured to capture the first image  304  of the calibration system  300 . The first image  304  illustrates the first and second calibration lines c 1  and c 2  having the first and second observed widths w 1  and w 2 , respectively. Upon capturing the first image  304 , the image-capturing device  106  may be configured to transmit the first image  304  to the processor  108 . In an embodiment, a color model associated with the first image  304  is the RGB color model. 
     In an embodiment, the processor  108  may be configured to receive the first image  304  from the image-capturing device  106  and process the first image  304  to identify the first plurality of rows of pixels (such as the first and second rows of pixels r 1  and r 2 ) in the first image  304 . The first and second rows of pixels r 1  and r 2  may correspond to the first and second calibration lines c 1  and c 2 , respectively. In an embodiment, the processor  108  may be configured to identify the first and second rows of pixels r 1  and r 2  based on one or more inputs provided by the administrator utilizing one or more input/output ports (not shown) of the vehicle device  104 . In another embodiment, the processor  108  may be configured to convert the first image  304  from the RGB color model to another color model such as the HSV color model. Thereafter, the processor  108  may be further configured to filter the converted first image (not shown) to obtain a known color of the carpet  302 , and further process the filtered first image (shown in  FIGS. 4B and 4C ) to identify the first and second rows of pixels r 1  and r 2 . In another embodiment, the first and second rows of pixels r 1  and r 2  may also be identified by utilizing a crowdsourcing platform where other users (e.g., crowd workers) may take up the related tasks, identify the first and second rows of pixels r 1  and r 2 , and upload the identified first and second rows of pixels r 1  and r 2  onto the crowdsourcing platform in an online manner. The afore-mentioned method for identifying the first and second rows of pixels r 1  and r 2  has been described in detail in conjunction with  FIGS. 4A-4C . 
     Further, in an embodiment, the processor  108  may be configured to estimate the focal length of the image-capturing device  106  based on one of the first calibration line c 1  or the second calibration line c 2 . Further, it is apparent to a person skilled in the art that the focal length F may be estimated by utilizing a first equation (1) as shown below: 
     
       
         
           
             
               
                 
                   F 
                   = 
                   
                     
                       
                         w 
                         k 
                       
                       * 
                       
                         d 
                         k 
                       
                     
                     W 
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
     Further, the first equation (1) may be modified to a second equation (2) as shown below:
 
 F*W=w   k   *d   k   (2)
 
     In the estimation of the focal length F based on the first calibration line c 1 , w k  indicates the first observed width w 1 , d k  indicates the second distance x, and W indicates the known width of the first calibration line c 1 . Thus, the second equation (2) may be modified to a third equation (3) as shown below:
 
 F*W=w   1   *x   (3)
 
     Similarly, in the estimation of the focal length F based on the second calibration line c 2 , w k  indicates the second observed width w 2 , d k  indicates the third distance y, and W indicates the known width of the second calibration line c 2 . Thus, the second equation (2) may be modified to a fourth equation (4) as shown below:
 
 F*W=w   2   *y   (4)
 
     As the first and second calibration lines c 1  and c 2  may be associated with equal known widths W and the focal length F may be a constant value for the image-capturing device  106 , the left-hand sides (LHSs) of the third equation (3) and the fourth equation (4) are equal. Hence, based on the third equation (3) and the fourth equation (4), a fifth equation (5) may be obtained that is shown below:
 
 w   1   *x=w   2   *y   (5)
 
where, y=d+x
 
     Further, the fifth equation (5) may be modified to obtain a sixth equation (6) as shown below: 
     
       
         
           
             
               
                 
                   x 
                   = 
                   
                     
                       d 
                       * 
                       
                         w 
                         2 
                       
                     
                     
                       ( 
                       
                         
                           w 
                           1 
                         
                         - 
                         
                           w 
                           2 
                         
                       
                       ) 
                     
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
           
         
       
     
     Thus, the processor  108  may be configured to estimate the second distance x based on the known distance d, the first observed width w 1 , and the second observed w 2 . Similarly, the processor  108  may be configured to estimate the third distance y based on the second distance x and the known distance d. Further, in an embodiment, the processor  108  may be configured to estimate the focal length of the image-capturing device  106  based on the first equation (1). 
     Further, as illustrated in  FIG. 3B , the first observed width w 1  may be greater than the second observed width w 2  since the second distance x is less than third distance y. The difference between the first and second observed widths w 1  and w 2  may be represented by δ w . Since the first and second calibration lines c 1  and c 2  are centered with respect to the axis of the image-capturing device  106 , the difference δ w  may be symmetric. Thus, δ w /2 may be estimated by utilizing a seventh equation (7) as shown below: 
                       δ   w     2     =     h   *   cot   ⁢           ⁢   θ             (   7   )               
where,
 
h indicates the second distance between the first and second rows of pixels (r 2 −r 1 ), and θ indicates the angle of the isosceles trapezoid.
 
     Thus, the difference δ w  may be estimated by utilizing an eighth equation (8) as shown below:
 
δ 2 =2 *h *cot θ  (8)
 
     In an embodiment, for one or more rows of pixels above the first row of pixels r 1 , a width of a corresponding line may be reduced. Thus, for a third line (not shown) corresponding to a fifth row of pixels r 5  that is between the first and second rows of pixels r 1  and r 2  in the first image  304 , a width of the third line may be less than the first observed width w 1  and more than the second observed width w 2 . Hence, the width of the third line (hereinafter, “the third width”) may be estimated based on a change in width δ w ′ of the third line with respect to either the first calibration line c 1  or the second calibration line c 2 . For example, the third width w 3  of the third line with respect to the first calibration line c 1  may be estimated by utilizing a ninth equation (9) as shown below:
 
 w   3   =w   1 −δ w ′  (9)
 
where, δ w ′=2*(r 5 −r 1 )*cot θ.
 
     Similarly, a width of a line corresponding to an “i th ” row of pixels r i  above the first row of pixels r 1  may be estimated by utilizing a tenth equation (10) as shown below:
 
 w   i   =w   1 −2*( r   i   −r   1 )*cot θ  (10)
 
where, (r i −r 1 ) indicates the second distance between the “i th ” row of pixels r i  and the first row of pixels r 1 .
 
     In an embodiment, for one or more rows of pixels below the first row of pixels, a width of a corresponding line may be increased. Hence, a width of a line corresponding to a “j th ” row of pixels r j  below the first row of pixels r 1  may be estimated by utilizing an eleventh equation (11) as shown below:
 
 w   j   =w   1 +2*( r   1   −r   j )*cot θ  (11)
 
where, (r 1 −r j ) indicates the second distance between the first row of pixels r 1  and the “j th ” row of pixels r j .
 
     Similarly, for one or more rows of pixels below the second row of pixels r 2 , a width of a corresponding line may be increased. Hence, the third width w 3  with respect to the second calibration line c 2  may be estimated by utilizing a twelfth equation (12) as shown below:
 
 w   3   =w   2 +δ w ′  (12)
 
     Similarly, a width of a line corresponding to a “p th ” row of pixels r p  below the second row of pixels r 2  may be estimated by utilizing a thirteenth equation (13) as shown below:
 
 w   p   =w   2 +2*( r   2   −r   p )*cot θ  (13)
 
where, (r 2 −r p ) indicates the second distance between the second row of pixels r 2  and the “p th ” row of pixels r p .
 
     Further, for one or more rows of pixels above the second row of pixels r 2 , a width of a corresponding line may be reduced. Hence, a width of a line corresponding to a “q th ” row of pixels r q  above the second row of pixels r 2  may be estimated by utilizing a fourteenth equation (14) as shown below:
 
 w   q   =w   2 −2*( r   q   −r   2 )*cot θ  (14)
 
where, (r q −r 2 ) indicates the second distance between the “q th ” row of pixels r q  and the second row of pixels r 2 .
 
     Thus, for each of the remaining rows of pixels (i.e., the second plurality of rows of pixels) in the first image  304 , the processor  108  may be configured to estimate the width of the corresponding line. In an embodiment, the processor  108  may be further configured to estimate the fifth distance of a line corresponding to each remaining row of pixels from the vehicle  102  based on the corresponding estimated width, the known width W, and the focal length F. For example, the fifth distance ds of a line corresponding to an “s th ” row of pixels r s  of the remaining rows of pixels from the vehicle  102  may be estimated by utilizing a fifteenth equation (15) as shown below: 
                     d   s     =       W   *   F       w   s               (   15   )               
where,
 
w s  indicates the estimated width of a line corresponding to the “s th ” row of pixels r s .
 
     In an embodiment, based on the estimated distances, the processor  108  may be configured to store the second and third distances x and y associated with the first and second rows of pixels r 1  and r 2 , respectively, and the fifth distance associated with each remaining row of pixels as the distance data set in the memory  110 . In another embodiment, the processor  108  may be configured to store the distance data set in the database server  122 . The vehicle device  104  may utilize the distance data set to predict the first distance of the first object in the implementation phase i.e., in the real-time driving scenarios. 
     In the implementation phase, when the vehicle  102  is traversing the road, the image-capturing device  106  may be configured to continuously capture the one or more images of the various objects that are present in front of the vehicle  102 . For example, the image-capturing device  106  may capture the second image of the first object on the road and transmit the second image to the processor  108 . 
     The processor  108  may be configured to receive the second image from the image-capturing device  106  and process the second image to detect the first object in the second image. The processor  108  may be further configured to detect the first bottom edge of the first object in the second image. In an embodiment, the processor  108  may perform an object detection operation on the second image to detect the first object in the second image. Examples of the object detection may include a Haar based object detection, a histogram of oriented gradients (HOG) based object detection, an HOG and support vector machines (SVM), i.e., the HOG-SVM based object detection, deep learning based object detection (using a convolutional neural network (CNN) approach, a region-of-interest-CNN i.e., an R-CNN approach, a fast R-CNN approach, or a faster R-CNN approach), or any combination thereof. The object detection operation may output a first rectangular bounding-box (shown in  FIGS. 5A and 5B ) that may include the first object in the second image. The first bottom edge of the first rectangular bounding-box may indicate a line of contact of the first object with the ground plane i.e., the first bottom edge of the first rectangular bounding-box may lie on the ground plane, as viewed from the image-capturing device  106 . 
     Upon detection of the first bottom edge, the processor  108  may be configured to identify the third row of pixels in the second image associated with the first bottom edge. In an embodiment, based on the third row of pixels, the processor  108  may be configured to retrieve the first distance value from the memory  110 . In another embodiment, based on the third row of pixels, the processor  108  may transmit the query to the database server  122  to retrieve the first distance value. The first distance value may correspond to the fourth row of pixels in the first image  304  having the row number that is equal to the row number of the third row of pixels in the second image. Further, the processor  108  may predict the first distance of the first object from the vehicle  102  based on the first distance value. In an embodiment, the first distance may be a distance of the first object from the image-capturing device  106 . A distance of the first object from a front edge of the vehicle  102  may be determined based on at least the first distance and a length of bonnet of the vehicle  102 . 
     Further, the processor  108  may be configured to generate the warning message based on the first distance of the first object and communicate the warning message to the driver of the vehicle  102 . For example, if the first distance is less than the threshold value, the processor  108  may generate the warning message indicating the impending collision of the vehicle  102  with the first object, and communicate the warning message to the driver for offering the driving assistance in real-time that may help to prevent the impending collision. In some embodiments, the processor  108  may generate the warning message based on the first distance and speed of the vehicle  102 . The processor  108  may communicate the warning message to the driver by communicating an SMS, an audio message, a video message, a haptic message, or the like. 
     In some embodiments, other than the first object, the first image may include various other objects. Distances of the various other objects captured in the second image may be predicted by executing a process that is similar to the process for predicting the first distance of the first object from the vehicle  102 . Further, although the disclosure describes use of a single image-capturing device (e.g., the image-capturing device  106 ) it will be apparent to a person skilled in the art that the scope of the disclosure is not limited to the use of the single image-capturing device. In some embodiments, a plurality of image-capturing devices having same or different focal lengths may be utilized. 
     Although the disclosure describes the prediction of distances of the various objects in front of the vehicle  102 , it will be apparent to a person skilled in the art that the scope of the disclosure is not limited to the prediction of distances of the various objects that are in the front of the vehicle  102 . In some embodiments, distances of various objects (that are behind or sideways to the vehicle  102 ) may also be predicted in the similar manner as described above. For predicting the distances of the various objects (that are behind the vehicle  102 ), a rear image-capturing device (not shown) may be installed on a rear side of the vehicle  102 . Similarly, for predicting the distances of the various objects (that are sideways to the vehicle  102 ), one or more sideway image-capturing devices (not shown) may be installed on each side of the vehicle  102 . 
       FIGS. 4A-4C , collectively, illustrate identification of the first and second rows of pixels r 1  and r 2  corresponding to the first and second calibration lines c 1  and c 2 , respectively, in accordance with an exemplary embodiment of the disclosure. As illustrated in  FIG. 4A , the image-capturing device  106  installed on the front windshield  200  may be configured to capture the first image  304  of the calibration system  300 . The image-capturing device  106  may be further configured to transmit the first image  304  to the processor  108 . The first image  304  may include the carpet  302  and the first and second calibration lines c 1  and c 2  drawn on the carpet  302 . The first and second calibration lines c 1  and c 2  may be at the second distance x and the third distance y, respectively, from the vehicle  102 . As illustrated in  FIG. 4A , the first and second calibration lines c 1  and c 2  may divide the carpet  302  into a plurality of sections such as sections  402   a - 402   c . For simplicity of the ongoing discussion, a color of the carpet  302  used in the calibration system has been assumed as green color. 
     Further, the color model associated with the first image  304  may be the RGB color model. However, the separation of color (i.e., separation of the green color of the carpet  302  from other colors in the first image  304 ) may be more distinct in the HSV color model as compared to the RGB color model. Thus, the processor  108  may be configured to convert the first image  304  from the RGB color model into the HSV color model. The processor  108  may be further configured to filter the converted first image to distinctly obtain the green color sections (i.e., the sections  402   a - 402   c ) in the converted first image. The sections  402   a - 402   c  of green color in the first image  304  have been illustrated as sections  404   a - 404   c  of black color in the filtered first image  406  (as illustrated in  FIG. 4B ). Further, the filtered first image  406  may include a section  408   a  (between the sections  404   a  and  404   b ) and a section  408   b  (between the sections  404   b  and  404   c ). The sections  408   a  and  408   b  may be indicative of the first and second calibration lines c 1  and c 2 , respectively. 
     The processor  108  may be further configured to perform an edge detection operation on the filtered first image  406  to identify a set of lines  410   a  passing through the section  408   a  and a set of lines  410   b  passing through the section  408   b . The sets of lines  410   a  and  410   b  are illustrated in  FIG. 4C . The sets of lines  410   a  and  410   b  may be candidate lines for the first and second calibration lines c 1  and c 2 , respectively. The processor  108  may be further configured to perform a Hough transform operation on the filtered first image  406  to identify first and second reference lines from the sets of lines  410   a  and  410   b  that correspond to the first and second calibration lines c 1  and c 2 , respectively. In an example, the first and second reference lines may be identified from the sets of lines  410   a  and  410   b , respectively, based on a slope of each line. The processor  108  may identify the first and second rows of pixels r 1  and r 2  based on the first and second reference lines, respectively. In an example, the processor  108  may identify the first and second rows of pixels r 1  and r 2  based on coordinates of the first and second reference lines, respectively. 
       FIG. 5A  illustrates the second image  500  including objects captured by the image-capturing device  106 , in accordance with an exemplary embodiment of the disclosure. The second image  500  may include objects  502   a - 502   d  present on the road in front of the vehicle  102 . For example, the objects  502   a  and  502   b  are cars, the object  502   c  is a truck, and the object  504   d  is a bike. In an embodiment, the object  502   a  is the first object. In another embodiment, the object  502   b  is the first object. In another embodiment, the object  502   c  is the first object. In another embodiment, the object  502   d  is the first object. The second image  500  may further include rectangular bounding-boxes  504   a - 504   d  around the borders of the objects  502   a - 502   d  indicating likely positions of the objects  502   a - 502   d  in the second image  500 , respectively. The rectangular bounding-boxes  504   a - 504   d  may be obtained as a result of the object detection operation performed by the processor  108  on the second image  500 . Examples of the object detection may include a Haar based object detection, a histogram of oriented gradients (HOG) based object detection, an HOG and support vector machines (SVM) i.e., the HOG-SVM based object detection, deep learning based object detection (using a convolutional neural network (CNN) approach, a region-of-interest-CNN, i.e., an R-CNN approach, a fast R-CNN approach, or a faster R-CNN approach), or any combination thereof. 
       FIG. 5B  illustrates the predicted distances of the objects  502   a - 504   d  from the vehicle  102 , in accordance with an exemplary embodiment of the disclosure. As illustrated in  FIG. 5B , the object  502   a  is at a distance of 9 m from the vehicle  102 , the object  502   b  is at a distance of 7 m from the vehicle  102 , the object  502   c  is at a distance of 11 m from the vehicle  102 , and the object  502   d  is at a distance of 8.5 m from the vehicle  102 . The predicted distance (i.e., the first distance) of each object in front of the vehicle  102  may be presented to the driver of the vehicle  102 . Further, based on at least the predicted distance of each object, the warning message may be generated and communicated to the driver. For example, if the predicted distances of any of the objects  502   a - 502   d  (such as the object  502   a ) is less than the threshold value, then a time to collision of the vehicle  102  with the object  502   a  may be determined. The time to collision may be determined based on the predicted distance and a relative speed between the vehicle  102  and the object  502   a . Based on the determined time to collision, the warning message indicating the impending collision may be generated and communicated to the driver of the vehicle  102 . The warning message may be communicated in the form of a text message, an audio signal, a video signal, or the like. In an embodiment, the predicted distance of each object may indicate a distance between the bottom edge of the corresponding object and the front end of the vehicle  102 . In another embodiment, the predicted distance of each object may indicate a distance between the bottom edge of the corresponding object and the image-capturing device  106  of the vehicle device  104  installed in the vehicle  102 . 
       FIGS. 6A and 6B  illustrate a flow chart  600  of a method for predicting the first distance of the first object from the vehicle  102 , in accordance with an exemplary embodiment of the disclosure. The distance prediction mechanism may detect a presence of the first object in front of the moving vehicle (e.g., the vehicle  102 ) and may predict the distance of the first object from the moving vehicle. In an embodiment, the distance prediction mechanism may be the vehicle device  104  that includes the image-capturing device  106 , the processor  108 , and the memory  110 . The vehicle device  104  may be calibrated in the calibration phase to predict the first distance in the implementation phase. The vehicle device  104  may be calibrated by way of the calibration system  300  that may include the first and second calibration lines c 1  and c 2  drawn on the carpet  302  at the known distance d from each other. 
     At  602 , the first image  304  of the calibration system is captured. In an embodiment, the image-capturing device  106  may be configured to capture the first image  304 . The first image  304  may include the first and second calibration lines c 1  and c 2 . The image-capturing device  106  may be configured to transmit the first image  304  (i.e., image data associated with the first image  304 ) to the processor  108 . 
     At  604 , the first and second rows of pixels r 1  and r 2  corresponding to the first and second calibration lines c 1  and c 2 , respectively, are identified in the first image  304 . In an embodiment, the processor  108  may be configured to identify the first and second rows of pixels r 1  and r 2  based on the first image  304 . 
     At  606 , the second distance x and the third distance y of the first and second calibration lines c 1  and c 2 , respectively, from the vehicle  102  is estimated. In an embodiment, the processor  108  may be configured to estimate the second distance x and the third distance y of the first and second calibration lines c 1  and c 2 , respectively, from the vehicle  102  based on the known distance d, the first observed width w 1 , and the second observed width w 2 . 
     At  608 , the focal length F of the image-capturing device  106  is estimated. In an embodiment, the processor  108  may be configured to estimate the focal length F of the image-capturing device  106  based on at least the first calibration line c 1  or the second calibration c 2 . For example, the processor  108  may estimate the focal length F of the image-capturing device  106  based on the second distance x and the known width W of the first calibration line c 1 , and the first observed width w 1 . 
     At  610 , the width of the line corresponding to each remaining row of pixels in the first image  304  is estimated. In an embodiment, the processor  108  may be configured to estimate, for each remaining row of pixels in the first image  304 , the width of the corresponding line based on the fourth distance between the corresponding row of pixels and the first or the second row of pixels r 1  or r 2 , the first and second observed widths w 1  and w 2 , and the first angle θ. The remaining rows of pixels in the first image  304  may correspond to the rows of pixels in the first image  304  apart from the first and second rows of pixels r 1  and r 2 . 
     At  612 , the fifth distance of a line corresponding to each remaining row of pixels in the first image  304  from the vehicle  102  is estimated. In an embodiment, the processor  108  may be configured to estimate the fifth distance of a line corresponding to each remaining row of pixels in the first image  304  from the vehicle  102  based on the known width W, the estimated width of the corresponding line, and the focal length F. Thus, each row of pixels in the first image  304  may have a corresponding distance from the vehicle  102  associated with it. 
     At  614 , the distance data set is stored in the memory  110  or the database server  122 . In an embodiment, the processor  108  may be configured to store the distance data set in the memory  110  or the database server  122 . The distance data set may include the second and third distances x and y associated with the first and second rows of pixels in the first image  304  and the estimated distance associated with each remaining row of pixels in the first image  304 . Thus, the vehicle device  104  may be calibrated to predict distances of the various objects based on distance values included in the distance data set that may be stored in the memory  110  or the database server  122 . 
     The implementation phase of the disclosure may correspond to the vehicle  102  traversing the road. At  616 , the second image  500  of the first object (e.g., the objects  502   a - 502   d ) present on the road in front of the vehicle  102  is captured. In an embodiment, the image-capturing device  106  may be configured to capture the second image  500 , as the vehicle  102  traverses the road, and transmit the second image  500  (i.e., image data associated with the second image  500 ) to the processor  108 . The second image  500  includes the first object (e.g., the objects  502   a - 502   d ). 
     At  618 , the first bottom edge of the first object is detected in the second image  500 . In an embodiment, the processor  108  may be configured to detect the first bottom edge of the first object based on the second image  500 . At  620 , the third row of pixels in the second image  500  corresponding to the first bottom edge is identified. In an embodiment, the processor  108  may be configured to identify the third row of pixels in the second image  500  corresponding to the first bottom edge. At  622 , the first distance value from the memory  110  or the database server  122  is retrieved. In an embodiment, the processor  108  may be configured to retrieve the first distance value from the memory  110  based on the third row of pixels. In another embodiment, based on the third row of pixels, the processor  108  may be configured to transmit the query to the database server  122  to retrieve the first distance value. The first distance value may correspond to the fourth row of pixels in the first image  304  whose row number is equal to the row number of the third row of pixels in the second image  500 . 
     At  624 , the first distance of the first object from the vehicle  102  is predicted. In an embodiment, the processor  108  may be configured to predict the first distance of the first object from the vehicle  102  based on the retrieved first distance value. At  626 , the warning message is generated. In an embodiment, the processor  108  may be configured to generate the warning message based on the predicted first distance. For example, the processor  108  may generate the warning message when the first distance is less than the threshold value. At  628 , the warning message is communicated to the driver of the vehicle  102 . In an embodiment, the processor  108  may be configured to communicate the warning message to the driver of the vehicle  102  indicating the impending collision. 
       FIG. 6C  illustrates a flowchart  630  of a method for identifying the first and second rows of pixels r 1  and r 2  corresponding to the first and second calibration lines c 1  and c 2 , respectively, in accordance with an exemplary embodiment of the disclosure. The processor  108  may be configured to receive the first image  304  of the calibration system  300  including the first and second calibration lines c 1  and c 2  from the image-capturing device  106 . The color model associated with the first image  304  is the RGB color model. However, the separation of color (e.g., the green color of the carpet  302 ) is more distinct in the HSV color model as compared to the RGB color model. 
     At  632 , the first image  304  is converted from the RGB color model to the HSV color model. In an embodiment, the processor  108  may be configured to convert the first image  304  from the RGB color model to the HSV color model. At  634 , the converted first image is filtered to obtain the green color associated with the carpet  302 . In an embodiment, the processor  108  may be configured to filter the converted first image to obtain the green color associated with the carpet  302  of the calibration system  300 . At  636 , the first and second rows of pixels r 1  and r 2  corresponding to the first and second calibration lines c 1  and c 2 , respectively, are identified from the filtered first image  406 . In an embodiment, the processor  108  may be configured to identify the first and second rows of pixels r 1  and r 2  corresponding to the first and second calibration lines c 1  and c 2 , respectively, from the filtered first image  406 . 
       FIG. 7  is a block diagram that illustrates a computer system  700  for calibrating a fixed image-capturing device (such as the image-capturing device  106  or  112 ) of a vehicle (such as the vehicle  102 ) and predicting a distance of an object (such as the first object) from the vehicle, in accordance with an exemplary embodiment of the disclosure. An embodiment of the disclosure, or portions thereof, may be implemented as computer readable code on the computer system  700 . In one example, the vehicle device  104 , the vehicle device  114 , the application server  120 , and the database server  122  of  FIG. 1  may be implemented in the computer system  700  using hardware, software, firmware, non-transitory computer readable media having instructions stored thereon, or a combination thereof and may be implemented in one or more computer systems or other processing systems. Hardware, software, or any combination thereof may embody modules and components used to implement the methods of  FIGS. 6A-6C . 
     The computer system  700  may include a processor  702  that may be a special purpose or a general-purpose processing device. The processor  702  may be a single processor, multiple processors, or combinations thereof. The processor  702  may have one or more processor “cores.” Further, the processor  702  may be connected to a communication infrastructure  704 , such as a bus, a bridge, a message queue, multi-core message-passing scheme, the communication network  124 , or the like. The computer system  700  may further include a main memory  706  and a secondary memory  708 . Examples of the main memory  706  may include RAM, ROM, and the like. The secondary memory  708  may include a hard disk drive or a removable storage drive (not shown), such as a floppy disk drive, a magnetic tape drive, a compact disc, an optical disk drive, a flash memory, or the like. Further, the removable storage drive may read from and/or write to a removable storage device in a manner known in the art. In an embodiment, the removable storage unit may be a non-transitory computer readable recording media. 
     The computer system  700  may further include an input/output (I/O) port  710  and a communication interface  712 . The I/O port  710  may include various input and output devices that are configured to communicate with the processor  702 . Examples of the input devices may include a keyboard, a mouse, a joystick, a touchscreen, a microphone, and the like. Examples of the output devices may include a display screen, a speaker, headphones, and the like. The communication interface  712  may be configured to allow data to be transferred between the computer system  700  and various devices that are communicatively coupled to the computer system  700 . Examples of the communication interface  712  may include a modem, a network interface, i.e., an Ethernet card, a communication port, and the like. Data transferred via the communication interface  712  may be signals, such as electronic, electromagnetic, optical, or other signals as will be apparent to a person skilled in the art. The signals may travel via a communications channel, such as the communication network  124 , which may be configured to transmit the signals to the various devices that are communicatively coupled to the computer system  700 . Examples of the communication channel may include a wired, wireless, and/or optical medium such as cable, fiber optics, a phone line, a cellular phone link, a radio frequency link, and the like. The main memory  706  and the secondary memory  708  may refer to non-transitory computer readable mediums that may provide data that enables the computer system  700  to implement the methods illustrated in  FIGS. 6A-6C . 
     Various embodiments of the disclosure provide the vehicle device  104  for calibrating the fixed image-capturing device (such as the image-capturing device  106 ) of the vehicle  102 . The vehicle device  104  may be configured to identify the first plurality of rows of pixels (such as the first and second rows of pixels r 1  and r 2 ) in the first image  304 . The first image  304  may be captured by the image-capturing device  106 . The first image  304  may include the plurality of calibration lines (such as the first and second calibration lines c 1  and c 2 ). The first calibration line c 1  of the plurality of lines is at the known distance d from the second calibration line c 2  of the plurality of lines. The first plurality of rows of pixels may correspond to the plurality of calibration lines. The vehicle device  104  may be further configured to estimate the second distance x and the third distance y of the first and second calibration lines c 1  and c 2 , respectively, from the image-capturing device  106 . The second distance x and the third distance y are estimated based on at least the known distance d, the first observed width w 1 , and the second observed width w 2 . The vehicle device  104  may be further configured to estimate the focal length F of the image-capturing device  106 . The focal length F may be estimated based on at least the first calibration line c 1  or the second calibration line c 2 . The vehicle device  104  may be further configured to estimate the fifth distance of each row of pixels of the second plurality of rows of pixels in the first image  304  from the image-capturing device  106 . The fifth distance may be estimated based on at least the focal length F and the width (such as the third width w 3 ) of the line (such as the third line) corresponding to each row of pixels. The third width w 3  may be estimated based on at least the plurality of calibration lines and each row of pixels of the second plurality of rows of pixels. The first plurality of rows of pixels and the second plurality of rows of pixels may constitute the first image. The vehicle device  104  may be further configured to store the distance data set including at least the second distance x, the third distance y, and the fifth distance in a memory (such as the memory  110 ). Further, a distance of an object from another object may be predicted based on the stored distance data set and an image of the object. 
     Various embodiments of the disclosure provide the vehicle device  104  for predicting the first distance of the first object from the vehicle  102 . In an embodiment, the image-capturing device  106  of the vehicle device  104  may be configured to capture the image (such as the second image  500 ) of the first object. The processor  108  of the vehicle device  104  may be configured to detect the first bottom edge of the first object based on the second image. The processor  108  may be further configured to identify the row of pixels (such as the third row of pixels) in the second image  500  corresponding to the detected bottom edge. The processor  108  may be further configured to predict the first distance of the first object from the vehicle  102  based on a distance value retrieved from the distance data set that may be stored in the memory  110  of the vehicle device  104  or a separate memory device installed in the vehicle  102 . The distance value may be retrieved from the distance data set based on at least the third row of pixels. The distance data set may be estimated by executing one or more calibration operations. For example, the plurality of rows of pixels (such as first plurality of rows of pixels) in the image (such as the first image  304 ) including the plurality of calibration lines may be identified. The first plurality of rows of pixels may correspond to the plurality of calibration lines. The first calibration line c 1  of the plurality of calibration lines is the known distance d from the second calibration line c 2  of the plurality of calibration lines. Further, the second distance x and the third distance y of the first and second calibration lines c 1  and c 2 , respectively, from a second object of the calibration system  300  may be estimated. The second distance x and the third distance y are estimated based on at least the known distance d, the first observed width w 1 , and the second observed width w 2 . 
     Further, the distance data set for the plurality of rows of pixels (such as the second plurality of rows of pixels) in the first image  304  may be estimated based on at least the width (such as the third width w 3 ) of the line (such as the third line) corresponding to each of the second plurality of rows of pixels and one of the first calibration line c 1  or the second calibration line c 2 . The third width w 3  may be estimated based on at least the plurality of calibration lines and each row of pixels of the second plurality of rows of pixels. The distance data set may further include at least the second distance x and the third distance y. The first image  304  may comprise the first plurality of rows of pixels and the second plurality of rows of pixels. The retrieved distance value may be associated with the fourth row of pixels in the first image  304  having a row number that is equal to a row number of the third row of pixels in the second image  500 . Upon prediction of the first distance of the first object from the vehicle  102 , the processor  108  may be configured to generate the warning message based on at least the predicted distance. The processor  108  may be further configured to communicate the warning message to the driver of the vehicle  102  indicating the impending collision. 
     Various embodiments of the disclosure provide a non-transitory computer readable medium having stored thereon, computer executable instructions, which when executed by a computer, cause the computer to execute operations for calibrating the fixed image-capturing device (such as the image-capturing device  106 ) of the vehicle  102 . The operations include identifying, by the vehicle device  104 , the first plurality of rows of pixels (such as the first and second rows of pixels r 1  and r 2 ) in the first image  304 . The first image  304  may be captured by the image-capturing device  106 . The first image  304  may include the plurality of calibration lines (such as the first and second calibration lines c 1  and c 2 ). The first calibration line c 1  of the plurality of lines is at the known distance d from the second calibration line c 2  of the plurality of lines. The first plurality of rows of pixels may correspond to the plurality of calibration lines. The operations further include estimating, by the vehicle device  104 , the second distance x and the third distance y of the first and second calibration lines c 1  and c 2 , respectively, from the image-capturing device  106 . The second distance x and the third distance y are estimated based on at least the known distance d, the first observed width w 1 , and the second observed width w 2 . The operations further include estimating, by the vehicle device  104 , the focal length F of the image-capturing device  106 . The focal length F may be estimated based on at least the first calibration line c 1  or the second calibration line c 2 . The operations further include estimating, by the vehicle device  104 , the fifth distance of each row of pixels of the second plurality of rows of pixels in the first image  304  from the image-capturing device  106 . The fifth distance may be estimated based on at least the focal length F and the width (such as the third width w 3 ) of the line (such as the third line) corresponding to each row of pixels. The third width w 3  may be estimated based on at least the plurality of calibration lines and each row of pixels of the second plurality of rows of pixels. The first plurality of rows of pixels and the second plurality of rows of pixels may constitute the first image. The operations further include storing, by the vehicle device  104 , the distance data set including at least the second distance x, the third distance y, and the fifth distance in the memory (such as the memory  110 ). Further, a distance of an object from another object may be predicted based on the stored distance data set and an image of the object. 
     Various embodiments of the disclosure provide a non-transitory computer readable medium having stored thereon, computer executable instructions, which when executed by a computer, cause the computer to execute operations for predicting the first distance of the first object from the vehicle  102 . The operations include capturing, by the image-capturing device  106  of the vehicle device  104 , the image (such as the second image  500 ) of the first object. The operations further include detecting, by the processor  108  of the vehicle device  104 , the first bottom edge of the first object based on the second image  500 . The operations further include identifying, by the processor  108 , the row of pixels (such as the third row of pixels) in the second image  500  corresponding to the detected bottom edge. The operations further include predicting, by the processor  108 , the first distance of the first object from the vehicle  102  based on a distance value retrieved from the distance data set that may be stored in the memory  110  of the vehicle device  104  or a separate memory device installed in the vehicle  102 . The distance value may be retrieved from the distance data set based on at least the third row of pixels. 
     The distance data set may be estimated by executing one or more calibration operations. For example, the plurality of rows of pixels (such as first plurality of rows of pixels) in the image (such as the first image  304 ) including the plurality of calibration lines may be identified. The first plurality of rows of pixels may correspond to the plurality of calibration lines. The first calibration line c 1  of the plurality of calibration lines is the known distance d from the second calibration line c 2  of the plurality of calibration lines. Further, the second distance x and the third distance y of the first and second calibration lines c 1  and c 2 , respectively, from a second object of the calibration system may be estimated. The second distance x and the third distance y are estimated based on at least the known distance d, the first observed width w 1 , and the second observed width w 2 . 
     Further, the distance data set for the plurality of rows of pixels (such as the second plurality of rows of pixels) in the first image  304  may be estimated based on at least the width (such as the third width w 3 ) of the line (such as the third line) corresponding to each of the second plurality of rows of pixels and one of the first calibration line c 1  or the second calibration line c 2 . The third width w 3  may be estimated based on at least the plurality of calibration lines and each row of pixels of the second plurality of rows of pixels. The distance data set may further include at least the second distance x and the third distance y. The first image  304  may comprise the first plurality of rows of pixels and the second plurality of rows of pixels. The retrieved distance value may be associated with the fourth row of pixels in the first image  304  having a row number that is equal to a row number of the third row of pixels in the second image  500 . Upon prediction of the first distance of the first object from the vehicle  102 , the processor  108  may be configured to generate the warning message based on at least the predicted distance. The processor  108  may be further configured to communicate the warning message to the driver of the vehicle  102  indicating the impending collision. 
     The disclosed embodiments encompass numerous advantages. The disclosure provides various methods and systems for predicting the first distance of the first object from the vehicle  102  based on the distance data set that is estimated in the calibration phase. The first and second rows of pixels r 1  and r 2  are identified by way of line correspondences (i.e., a line (such as the first and second calibration lines c 1  and c 2 ) on the ground plane is mapped to a row of pixels in an image (such as the first image  304 )). Hence, the accuracy of identification of the first and second rows of pixels r 1  and r 2  is higher as compared to conventional distance prediction approaches that use point correspondences. Further, the calibration of the distance prediction mechanism is performed using a single frame (i.e., the first image  304  of the calibration system  300 ), thereby eliminating a need to have multiple frames (i.e., images) of a calibration system (e.g., the calibration system  300 ) for calibration. Further, based on the determined distance data set, the methods and the systems facilitate predicting far-away objects (e.g., objects at distances greater than 10 meters) with high accuracy. Further, since the distances of the distance data set may be estimated based on the plurality of calibration lines of the calibration system  300 , and the distances of various objects (such as the first object) may be predicted based on the distance data set, a need for knowing actual dimensions of the various objects beforehand for predicting distances from the vehicle  102  is eliminated. Thus, the method and the system of the present disclosure provide a more efficient, a more effective, and a more accurate way of predicting the first distance of the first object as compared to a conventional approach of distance prediction. 
     A person of ordinary skill in the art will appreciate that embodiments and exemplary scenarios of the disclosed subject matter may be practiced with various computer system configurations, including multi-core multiprocessor systems, minicomputers, mainframe computers, computers linked or clustered with distributed functions, as well as pervasive or miniature computers that may be embedded into virtually any device. Further, the operations may be described as a sequential process, however some of the operations may in fact be performed in parallel, concurrently, and/or in a distributed environment, and with program code stored locally or remotely for access by single or multiprocessor machines. In addition, in some embodiments, the order of operations may be rearranged without departing from the spirit of the disclosed subject matter. 
     Techniques consistent with the disclosure provide, among other features, systems and methods for predicting the first distance of the first object from the vehicle  102 . While various exemplary embodiments of the disclosed systems and methods have been described above, it should be understood that they have been presented for purposes of example only, and not limitations. It is not exhaustive and does not limit the disclosure to the precise form disclosed. Modifications and variations are possible in light of the above teachings or may be acquired from practicing of the disclosure, without departing from the breadth or scope. 
     While various embodiments of the disclosure have been illustrated and described, it will be clear that the disclosure is not limited to these embodiments only. Numerous modifications, changes, variations, substitutions, and equivalents will be apparent to those skilled in the art, without departing from the spirit and scope of the disclosure, as described in the claims.