Assessing one or more occurrences in an event

Abstract

A method 300 and a system 102 for assessing one or more occurrences are described In one example, a video input may be obtained. The video input may correspond to an event. Thereafter, based on the obtained video input, a set of successive time-stamped input images may be extracted. Each of the extracted input images may include one or more entities. Furthermore, one or more entities may be extracted from each of the successive time-stamped input images. Thereafter, based on the extracted one or more entities, the set of successive input images may be extracted to determine one or more occurrences in the video input. Hereinafter, the determined one or more occurrences in the video input may be assessed.

Description

ASSESSING ONE OR MORE OCCURRENCES IN AN EVENTTECHNICAL FIELD

[0001] The present disclosure generally relates to the field of image processing. More specifically, the present invention relates to improved and efficient approaches for accurately and efficiently assessing one or more occurrences in an eventBACKGROUND OF THE INVENTION

[0002] In sporting events, object tracking plays an important role. In sporting events like cricket, object tracking may include ball tracking and tracking the body movement of a player. Such tracking is required for accurate prediction of ball trajectory and player performance, respectively. Existing object tracking techniques for sporting events require great use of resources, such as multiple cameras, high processing resources, etc. Further, the object tracking techniques that do not require such high resources are extremely limited in their application and have large lag times. Moreover, the existing platforms that offer such object tracking techniques are single agenda applications. In other words, other than tracking objects, there is no scope available for audience to interact with the sporting event, such as predicting outcomes, or gamifying the predictions. There is, therefore, a requirement in the art for an application that overcomes at least some of the limitations mentioned above.SUMMARY OF THE INVENTION

[0003] This section is provided to introduce certain aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.

[0004] An aspect of the present disclosure may relate to a method for assessing one or more occurrences in an event. The method includes obtaining a video input. The video corresponds to an event. Thereafter, based on the obtained video input, the method includes extracting a set of successive time-stamped input images. Each of the extracted input images includes one or more entities. The method further includes extracting one or more entities from each of the successive time-stamped input images. Based on the extracted one or more entities, the method furthermore includes analysing the set of successive input images to determine one or more occurrences in the video input. Thereafter, the method includes assessing the determined one or more occurrences in the video input.

[0005] In an exemplary aspect of the present disclosure, the event is a sporting event. The sporting event may include one of a cricket event, an archery event, a javelin throw event, and a dart event.

[0006] In an exemplary aspect of the present disclosure, the method further includes obtaining the video input based on a user input.

[0007] In an exemplary aspect of the present disclosure, the method further includes obtaining the video input from a repository.

[0008] In an exemplary aspect of the present disclosure, the method further includes obtaining the video input from an image capturing device in real-time.

[0009] In an exemplary aspect of the present disclosure, the method further includes causing to display the obtained video input on a display device.

[0010] In an exemplary aspect of the present disclosure, the entity may be one of a projectile, a participant, and a combination thereof, and wherein the projectile is one of a ball, a javelin, an arrow, a dart, a disc, a pellet, and a combination thereof.

[0011] In an exemplary aspect of the present disclosure, the analysis of the set of successive input images based on the extracted one or more entities further includes determining a set of positions of the one or more entities in each of the frames of successive time-stamped input images. Based on the determined set of positions, the method further includes determining a movement of said one or more entities in the successive time-stamped input images.

[0012] In an exemplary aspect of the present disclosure, the step of determining the movement of the one or more entities in the successive time-stamped input images may include one of a determination of a trajectory of the projectile in the video input, a determination of a movement of the participant in the video input, a determination of one or more interactions between the projectile and the participant, and a combination thereof. The movement of the participant includes one of a resting stance, a moving stance, a stationary movement, a dynamic movement, and a combination thereof.

[0013] In an exemplary aspect of the present disclosure, the step of assessing the determined one or more occurrences in the video input may include one of assessing a trend of trajectory of one or more projectiles in the video input, determining variation between one or more trajectories of the one or more projectiles in the video input, determining one of a velocity, location, motion along a 2- dimensional (2D) plane, trajectory in 3 -dimensional (3D) plane, relative position, and a combination thereof, of the one or more projectiles, assessing a trend of movement patterns of the body of the participant, determining variations between movement patterns of the body of the participant during different events, determining one of a posture, step size, swing motion of the arm of the participant, and a combination thereof.

[0014] In an exemplary aspect of the present disclosure, the method further includes causing to display the assessed one or more occurrences in the video input on a display device

[0015] Another aspect of the present disclosure may relate to a system for assessing one or more occurrences in an event. The system comprises a processor. The system further comprises an assessment unit coupled to the processor. The assessment unit is configured to obtain a video input. The video corresponds to an event. The assessment unit is further configured to extract a set of successive time-stamped input images, and wherein each of the extracted input images comprises one or more entities, based on the obtained video input. Furthermore, the assessment unit is configured to extract one or more entities from each of the successive time-stamped input images. Based on the extracted one or more entities, the assessment unit is configured to analyse the set of successive input images to determine one or more occurrences in the video input. Thereafter, the assessment unit is configured to assess the determined one or more occurrences in the video input.

[0016] Yet another aspect of the present disclosure may relate to a non-transitory computer readable storage medium storing instructions for assessing one or more occurrences in an event. The instructions include an executable code which, when executed by a processing resource of a system, causes the system to obtain a video input. The video corresponds to an event. Thereafter, the instructions further cause the system to extract a set of successive time-stamped input images, based on the obtained video input. Each of the extracted input images includes one or more entities. The instructions further cause the system to extract one or more entities from each of the successive time- stamped input images. The instructions further cause the system to analyse the set of successive input images to determine one or more occurrences in the video input, based on the extracted one or more entities. The instructions further cause the system to assess the determined one or more occurrences in the video input.OBJECTS OF THE INVENTION

[0017] This section is provided to introduce certain objects and aspects of the present invention in a simplified form that are further described below in the description. In order to overcome at least a few problems associated with the known solutions as provided in the previous section, an object of the present invention is to substantially reduce the limitations and drawbacks of the prior arts as described hereinabove.

[0018] An object of the present invention is to provide a method and a system for assessing one or more occurrences in an event, such as a sporting event.

[0019] Another object of the invention is to provide a method to allow to track projectile in a sporting event and identify participant using a single image capturing device.

[0020] Yet another object of the invention is to provide accurate tracking with high accuracy.

[0021] Yet another object of the present invention is to provide a system and method that can be implemented without use of very high processing resources.BRIEF DESCRIPTION OF DRAWINGS

[0022] The accompanying drawings, which are incorporated herein, constitute a part of this disclosure. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components or circuitry commonly used to implement such components. Although exemplary connections between sub-components have been shown in the accompanying drawings, it will be appreciated by those skilled in the art that other connections may also be possible, without departing from the scope of the invention. All sub-components within a component may be connected to each other, unless otherwise indicated.

[0023] FIG. 1 illustrates an exemplary computing environment for assessing one or more occurrences in an event, in accordance with exemplary embodiments of the present invention;

[0024] FIG. 2 illustrates a block diagram of an exemplary computing device for assessing one or more occurrences in an event, in accordance with an exemplary implementation of the present disclosure; and

[0025] FIG. 3 illustrates a flowchart depicting an example method for assessing one or more occurrences in an event, in accordance with an exemplary implementation of the present disclosure.

[0026] The foregoing shall be more apparent from the following more detailed description of the disclosure.DETAILED DESCRIPTION

[0027] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, that embodiments of the present invention may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address any of the problemsdiscussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein. Example embodiments of the present invention are described below, as illustrated in various drawings.

[0028] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0029] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail.

[0030] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations may be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure.

[0031] The word “exemplary” as used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive — in a manner similar to the term “comprising” as an open transition word — without precluding any additional or other elements.

[0032] As used herein, the expression “and / or” includes any single item from items or a combination of items associated with the items. For example, a group of A, B and / or C includes only A, only B, only C, a combination of A and B, a combination of B and C, a combination of A and C or a combination of A, B and C.

[0033] As used herein, the expression “one or more of’ includes any single item in the list or a combination of items in the list. For example, one or more of A, B and C includes only A, only B, only C, a combination of A and B, a combination of B and C, a combination of A and C, a combination of A, B and C, a combination of multiple A and multiple B, a combination of multiple A, a single B and a single C, a combination of single A, multiple B and multiple C and any other such like combinations.

[0034] As used herein the expression “at least one of’ shall be interpreted as an inclusive term that includes at least one of the succeeding elements, and shall also include multiple of such elements in different combinations. For example, the term “an exemplary parameter comprising at least one of A, B and C” may imply that the exemplary parameter may comprise only {A} or only {B} or only {C}, or {A, B, C} collectively, and may also comprise various combinations of A, B, and C (with or without any other element such as D), such as {A, B}, {B, C}, {C, A}, {C, D}, {D, A} and any other such like combinations. Further, the expression shall also be construed to be include multiple instances of such elements for example {A, A}, {A. A, D}, {A, B, C, A, B, C}, {A, B, C, A, B, C,D}, etc.

[0035] As used herein, a “processing unit” or “processor” or “operating processor” or “processing resource” includes one or more processors, wherein processor refers to any logic circuitry for processing instructions. A processor may be a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors in association with a (Digital Signal Processing) DSP core, a controller, a microcontroller, Application Specific Integrated Circuits, Field Programmable Gate Array circuits, any other type of integrated circuits, etc. The processor may perform signal coding data processing, input / output processing, and / or any other functionality that enables the working of the system according to the present disclosure. More specifically, the processor or processing unit is a hardware processor.

[0036] As used herein, “a computing device”, “a user equipment”, “a user device”, “a smart-user- device”, “a smart-device”, “an electronic device”, “a mobile device”, “a handheld device”, “a wireless communication device”, “a mobile communication device”, “a communication device” may be any electrical, electronic and / or computing device or equipment, capable of implementing the features of the present disclosure. The user equipment / device may include, but is not limited to, a mobile phone, smart phone, laptop, a general-purpose computer, desktop, personal digital assistant, tablet computer, wearable device or any other computing device which is capable of implementing the features of the present disclosure. Also, the user device may contain at least one input means configured to receivean input from at least one of a transceiver unit, a processing unit, a storage unit, a detection unit and any other such unit(s) which are required to implement the features of the present disclosure.

[0037] As used herein, a “storage unit” or a “memory unit” refers to a machine or computer- readable medium including any mechanism for storing information in a form readable by a computer or similar machine. For example, a computer-readable medium includes read-only memory (“ROM”), random access memory (“RAM”), magnetic disk storage media, optical storage media, flash memory devices or other types of machine-accessible storage media. The storage unit stores at least the data that may be required by one or more units of the system to perform their respective functions.

[0038] As used herein “interface” or “user interface” refers to a shared boundary across which two or more separate components of a system exchange information or data. The interface may also be referred to a set of rules or protocols that define communication or interaction of one or more modules or one or more units with each other, which also includes the methods, functions, or procedures that may be called.

[0039] All modules, units, components used herein, unless explicitly excluded herein, may be software modules or hardware processors, the processors being a general-purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors in association with a DSP core, a controller, a microcontroller, Application Specific Integrated Circuits (ASIC), Field Programmable Gate Array circuits (FPGA), any other type of integrated circuits, etc.

[0040] In sporting events, object tracking plays an important role. In sporting events like cricket, object tracking may include ball tracking, and tracking a body movement of a player. Such tracking is required for accurate prediction of ball trajectory, and player performance, respectively. Existing object tracking techniques for sporting events require great use of resources, such as multiple cameras, high processing resources, etc. Further, the object tracking techniques that do not require such high resources are extremely limited in their application and have large lag times. Moreover, the existing platforms that offer such object tracking techniques are single agenda applications. In other words, other than tracking objects, there is no scope available for audience to interact with the sporting event, such as predicting outcomes, or gamifying the predictions. There is, therefore, a requirement in the art for an application that overcomes at least some of the limitations mentioned above.

[0041] One major challenge in the existing systems lies in the fact that as the speed of the object varies after its release, a regular camera is often unable to accurately capture it in each and every frame, often leading to distortions of the image of the object in certain frames. This distortion takesthe form of the objected appearing to be ‘elongated’ in certain frames - which greatly distorts the pixel data recorded and results in inaccuracy in the assessment as it gets difficult to extract the object.

[0042] In view of the aforementioned, embodiments of the present disclosure relate to approaches for assessing one or more occurrence in an event. In one example, a video input may be obtained. The video input may correspond to an event. Further, a set of successive time-stamped input images may be extracted from the video input. It may be noted that each of the extracted input images comprises one or more entities. Further, one or more entities may be extracted from each of the successive time- stamped input image. Thereafter, based on the extracted one or more entities, the set of successive input images may be extracted to determine one or more occurrences in the video input. Hereinafter, the determined one or more occurrences in the video input may be assessed.

[0043] As would be appreciated, the approaches of the present invention provide a number of technical advancements. For example, the present invention provides for an interface to assess the one or more occurrences in any event. The present invention further provides accurate and satisfactory results of assessment of one or more occurrences in an event.

[0044] For example, the user may either upload a video input or capture the video input in realtime of an event, such as a sporting event. The sporting event may entail interaction of a projectile and a participant. The video input may include one or more occurrences, wherein the participant may be interacting with the projectile. The participant, in certain case, may want to train themselves, and the present disclosure provides an efficient and an improved method and a system for assessing the movement of the participant and the manner in which the participant interacts with the projectile.

[0045] The conventional approaches entail usage of a substantial amount of processing resources.The conventional approaches also require the usage of LiDar (Light Detection and Ranging) sensor for assessing a 3D trajectory of the projectile.

[0046] On the other hand, using the approaches of the present subject matter, the system may be able to efficiently employ image recognition techniques to analyse the movement and trajectories of the participant, while interacting with the projectile, and thus may provide personalized and customized recommendations to the user. The present invention allows the tracking of movement of the projectile and further allows the tracking of the trajectory of the interaction of the projectile with the participant, using standard image capturing devices, without LiDAR sensor.

[0047] The present subject matter is further described with reference to the accompanying figures. Wherever possible, the same reference numerals are used in the figures and the following description to refer to the same or similar parts. It should be noted that the description and figures merely illustrate principles of the present subject matter. It is thus understood that variousarrangements may be devised that, although not explicitly described or shown herein, encompass the principles of the present subject matter. Moreover, all statements herein reciting principles, aspects, and examples of the present subject matter, as well as specific examples thereof, are intended to encompass equivalents thereof.

[0048] The manner in which the one or more occurrences are assessed in the event, is explained in detail with respect to FIGS. 1-3. It is to be noted that drawings of the present subject matter shown here are for illustrative purposes and are not to be construed as limiting the scope of the subject matter claimed.

[0049] Referring to FIG. 1, an exemplary computing environment 100 with an exemplary system 102 is shown. In one example, the exemplary computing environment 100 includes a system 102. The system 102 may be used for assessing one or more occurrences in an event, in accordance with an implementation of the present subject matter. Examples of such events may include, but are not limited to, a sporting event such as a cricket event, a javelin event, a football event, a volleyball event, and the like. For example, if the event is the cricket event, examples of such occurrences may include, but are not limited to, a player receiving the ball from a bowler, the player hitting the ball with a bat, the ball hitting the wickets, and the like. For another example, the event is the dart throwing event, examples of such occurrences may include, but are not limited to, the participant throwing the dart, the dart board receiving the dart, and the like.

[0050] Examples of such system 102 may include, but are not limited to, a personal computer, a handheld computer, a mobile device, and a portable computer. In one example, the system 102 may be used by a User. In such cases, the system 102 may also be referred to as the User Equipment (UE). In another example, the system 102 may be a part of a larger system (not depicted in FIG. 1) which may be used in image processing. In such cases, based on the inputs received, the system 102 may receive any input image, and assess one or more occurrences in an event on such input image. In yet another example, the system 102 may be any system capable of receiving inputs, processing it, and displaying output information.

[0051] Continuing further, as depicted in FIG. 1, the system 102 includes a processor 104. In one example, the processor 104 may be a dedicated special-function processor or a general -purpose processor which may be used by the system 102, in conjunction with other elements of the computing environment 100, to implement the features of the present disclosure.

[0052] The system 102 further includes an image capturing device 106. The image capturing device 106 may be capable of capturing an image or record a video, and providing the captured image or the recorded video to the system 102 for further processing. Examples of such image capturingdevices 106 may include, but are not limited to, a camera, a digital camera, a smart pen-based camera, a video camera, AR / VR set, and the like. In one example, the image capturing device 106 may be integrated within the system 102 and may be an in-built part of the system 102, such as in exemplary cases of a portable mobile phone or a computing device. In another example, the image capturing device 106 may be coupled to the system 102, such as in exemplary cases of a stand-alone camera, webcam, etc. being coupled to a computing device. However, it may be noted that all such examples are exemplary, and should not be construed to limit the scope of the present subject matter in any manner. Any other form of image capturing device 106 may also be integrated within or coupled to the system 102, and would lie within the scope of the present subject matter.

[0053] The system 102 may further include a display device 108. Based on the processing by the system 102 and signals generated by the system 102, the display device 108 may be capable of displaying the output. Further, examples of such display devices 108 may include, but are not limited to, a desktop, a mobile display, a television, and the like. The display device 108, in one example, as depicted in FIG. 1, may be integrated within and a part of the system 102. In another example, as would be described in conjunction with FIG. 2, the display device 108 may be coupled to the system 102.

[0054] Continuing further, the system 102 may be connected to a centralized server 110 over a network 112. The centralized server 110 may include an assessment unit 114.

[0055] As used herein, the centralized server 110 may be a set of one or more systems, which may be responsible, either individually or collectively, to perform a set of functions to enable the processing of visual inputs that may be received from the system 102, for assessing one or more occurrences in an event. Further, the centralized server 110 may be one of a local server, a remote server, or a combination thereof. Alternatively, the central server 110 may also be implemented as a network-based, cloud-based, or a software-based server.

[0056] Further, the assessment unit 114 may be implemented as a software component that may be configured to analyze one or more sets of data, on the basis of instructions received from the processor 104 of the system 102. Alternatively, the present disclosure also encompasses that the assessment unit 114 may also be a general or specific function hardware component, such as a processor, or a combination of both software and hardware components.

[0057] It may be noted that, as depicted in FIG. 1, the assessment unit 114 may be present in the centralized server 110, which in turn may be in communication with the system 102, however, it may be noted that such implementation is not to be construed to limit the scope of the present subject matterin any manner. In another example, the assessment unit 114 may be a part of the system 102 itself, which would be further explained later in conjunction with FIG. 2.

[0058] Further, as used herein, the network 112 may be one of a local area network (LAN) or a wide area network (WAN), i.e., the internet. The system 102 and the centralized server 110, as embodied herein, may be connected to the network 112 using at least one of a wired connection and a wireless connection, or a combination thereof. A wired connection may comprise an ethernet connection, an optical fibre connection, or any other wired network connection as may be known to a person ordinarily skilled in the art. Further, a wireless connection may comprise a connection based on at least one of the following wireless communication technologies, such as Wi-Fi, Li-Fi, Radio Frequencies, Infrared, Cellular Communication (3G, 4G, 5G), Bluetooth, Near Field Communication (NFC), satellite communication and / or any other wireless communication technology as may be known to a person ordinarily skilled in the art.

[0059] In operation, in one example, the image capturing device 106 may capture a video input (not depicted in FIG. 1) in real-time. As would be understood, the video input may include a plurality of successive input images. One such input image has been depicted in FIG. 1, as input image 116. The video input may be a part of an event, such as a sporting event. In the context of the sporting event being a cricket match, the video input may correspond to a part of the cricket match. For example, the video input may correspond to an over, which includes six deliveries, or a single delivery. The video input may encompass various scenarios, including but not limited to, a participant receiving a ball, a participant throwing a ball, a ball hitting the wicket, a participant throwing the ball for a wide.

[0060] It may be noted that all such examples are only illustrative, and in no manner to be construed to limit the scope of the present subject matter in any manner. The present invention may be implemented for any sporting event, and the video input may correspondingly refer to any part of such sporting event. All such examples would he within the scope of the present subject matter.

[0061] Continuing further, once the video input is obtained by the system 102, the assessment unit 114 may extract a set of successive time-stamped input images 116. Each of the extracted input images 116 may include one or more entities.

[0062] For example, as depicted in FIG. 1, the input image 116 may be one of the plurality of images, which may be extracted from the video input. As depicted in the input image 116, the image includes a projectile and a participant, referred to as entities.

[0063] It may be again noted that the input image 116, as depicted in FIG. 1 , is shown only for the purpose of illustration, and in no manner should be construed to limit the scope of the present subject matter in any manner. Further, it should be noted that, although the foregoing description hasbeen described in the context of the event being a cricket event, however, it may be noted that the same is done only for the sake of clarity and for ease of explanation. The present invention may be implemented for assessing one or more occurrences of any other event such as a javelin event, a football event, a volleyball event, and the like. All such variations and examples would be covered in such example.

[0064] Continuing further, once the set of successive time-stamped input images 116 have been extracted, the assessment unit 114 may extract one or more entities from each of the successive time- stamped input images 116. For example, from each of the successive time-stamped input images 116, the assessment unit 114 may extract the entities, such as the projectile and the participant.

[0065] Once the one or more entities have been extracted, the assessment unit 114 may analyse the set of successive input images 116 to determine one or more occurrences in the video input. Thereafter, the assessment unit 114 may assess the determined one or more occurrences in the video input.

[0066] The manner in which the system 102 may perform the assessment of the one or more occurrences in the video input, in conjunction with other details, is illustrated with further details in conjunction with FIGS. 2-3.

[0067] Referring to FIG. 2, a block diagram of an exemplary computing device 200 for assessing one or more occurrences in an event, in accordance with an implementation of the present subject matter, is shown.

[0068] In one example, the computing device 200 may be implemented as the system 102 as explained in FIG. 1. Further, the computing device 200 may be implemented as or within a plurality of devices, including, but not limited to a smartphone, a smartwatch, a smart pen, a digital camera, an AR / VR headset, a laptop or desktop computer, a computer-based kiosk, and / or any other computing device as may be known to a person ordinarily skilled in the art.

[0069] The computing device 200 may be used for assessing one or more occurrences in an event. Examples of such occurrences may include, but are not limited to, a javelin event, a football event, a volleyball event, and the like, and a combination thereof. Further, examples of such events may be one of a participant receiving a ball, a participant throwing a ball, a ball hitting the wicket, a participant throwing the ball for a wide, and the like.

[0070] Further, it may be noted that the present example and the foregoing description has been explained in context of assessing an occurrence in an event and the same is done merely for the sake of conciseness and ease of understanding, and in no manner should be construed to limit the scope of the present subject matter. The approaches of the present subject matter may be used for assessing anynumber of occurrences in any number of events, and all such examples would also lie within the scope of the present subject matter.

[0071] As depicted in FIG. 2, the computing device 200 may include processor(s) 104 and unit(s) 202 which may be connected to the processor 104. The unit 202 may further comprise sub-components for implementing one or more features of the present disclosure, such as, an assessment unit 114, a storage unit 208, an extraction unit 210, an analysis unit 212, a determination unit 214 and other unit(s) 216, wherein either, or all the sub-components may also be connected to the processor 104. It may be noted that the other unit(s) 216 may perform any function that may be ancillary to any of the units 112, 208, 210, 212, and 214 for assessing one or more occurrences in an event.

[0072] The computing device 200 may further include an Artificial Intelligence / Machine Learning (AI / ML) model 204. The AI / ML model 204 may be implemented as any artificial intelligence or machine learning model, known and understood to a person skilled in the art. The explanation for the same has not been provided here for the sake of brevity.

[0073] Further, the computing device 200 may be configured to obtain and store one or more sets of data 206, which may further comprise a video input 218, time-stamped input image(s) 220, entities 222, occurrence(s) 224, an event 226, and other data 228. It may be noted that the other data 228 may comprise any data that may be ancillary, and / or additional to the other sets of data comprised in the data 206, any or all of which may be stored or obtained by the computing device 200 to assess the one or more occurrences in an event.

[0074] As further depicted in FIG. 2, an image capturing device 230 and a display device 232 may be communicatively coupled to the computing device 200. In one example, the image capturing device 230 may be implemented as the image capturing device 106, and the display device 232 may be implemented as the display device 108 as depicted in and explained in conjunction with FIG. 1.

[0075] Examples of such image capturing devices 230 may include, but are not limited to, a smartphone, a digital camera, a smart pen-based camera, a video camera, AR / VR set, and the like. Further, examples of such display devices 232 may include, but are not limited to, a desktop, a mobile display, a television, and the like.

[0076] In one example, any or all of the image capturing devices 230 and / or the display devices 232 may be implemented in one or more of a wide range of user devices, such as phones, pens, watches, laptop, desktop, glasses, AR / VR headsets, etc. It may be further understood by a person ordinarily skilled in the art that the aforementioned list of devices is merely exemplary and not intended to limit the scope of the present subject matter in any manner. It may be further understood that the plurality of units 202 in the present subject matter may be implemented in any other deviceof the user to intelligently identify and correct one or more distortions in an image. All such examples would lie within the scope of the present subject matter.

[0077] In another example, the image capturing devices 230 and the display devices 232 may be implemented in different devices working in conjunction, or otherwise, e.g., the computing device 200 may be present in a user / viewer device to implement the features of the present invention. The computing device 200 may be a part of the user device / or may be independent of but in communication with the user device. In another implementation, the computing device 200 may reside in a server. In yet another implementation, the computing device 200 may reside partly in the server and partly in the user device.

[0078] In operation, in one example, a user (not depicted in FIG. 2) may initiate the process for assessment of one or more occurrences 224 in an event 226. In another example, the user may be a participant, with an intention to improve their skillset pertaining to an event. In yet another example, the user may be a coach, with an intention to assess the participant and evaluate their skillset.

[0079] In yet another example, the present subject matter may be implemented in the form of an application, which may be implemented on the computing device 200. The application may be a webbased or a mobile-based application. In yet another example, the application may include a Graphical User Interface (GUI), which may allow the user to initiate the process of assessment of one or more occurrences in the event.

[0080] In yet another example, the application may include a login page, which may allow the user to login using their credentials. In yet another example, the user may be provided with the credentials, based on a subscription, which may be purchased by the user.

[0081] However, it may be noted that all such examples are only exemplary and illustrative, and in no manner to be construed to limit the scope of the present subject matter in any manner. The present invention may be implemented in any other manner as well, and all such examples / variations would be covered within the scope of the present subject matter.

[0082] In one example, in operation, the user may provide a user input, or a trigger. The user input may be used for initiating the process for assessment of one or more occurrences 224 of the event 226. Once the user input is provided, the assessment unit 114 may obtain the video input. In an embodiment, the user may provide the user input at the interface.

[0083] In another example, the assessment unit 114 may be monitoring a video. The user may provide the user input during the course of streaming of the video. Once the user input is provided, the assessment unit 114 may initiate the process of assessment of the one or more occurrences in the event.

[0084] In another example, the assessment unit 114 may obtain the video input 218 from a repository. For example, once the user input is obtained by the assessment unit 114, the assessment unit 114 may provide an option to the user, for uploading any video input 218 corresponding to the event 226. In one example, the event 226 may be a sporting event. The sporting event includes one of a cricket event, an archery event, a javelin throw event, a dart event or any other sporting event.

[0085] In an embodiment, the video input 218 includes similar file types, or different file types. In an embodiment, a resolution of the set of input images 220, as included in the video input 218, may be similar or different. In an embodiment, the file type and / or resolution of the set of input images 220 used may be determined by a total size of the files. In an embodiment, the video input 218 may not exceed a predetermined size. In a preferred embodiment, the video input 218 may be a video file having a resolution of at least 60 frames per second (fps). In an embodiment, the video input 218 may be associated with a predetermined time duration or with a predetermined sequence.

[0086] In one example, the video input 218 may be a part of the match or sporting event. For instance, if the event is a cricket match, the video input 218 may correspond to deliveries in an over (6 balls). In another example, the video input 218 may be a whole match or sporting event video. In yet another example, the video input 218 may be a single delivery of the event.

[0087] In another example, the image capturing device 230, such as a still camera, a video camera, etc., may be communicatively coupled to the computing device 200. The image capturing device 230 may capture the video in real-time, and provide the video input 218 to the computing device 200.

[0088] Although the foregoing description has been described in the context of the event being a cricket event, however, it may be noted that the same is done only for the sake of clarity and for ease of explanation. The present invention may be implemented for assessing one or more occurrences of any other event such as a javelin event, a football event, a volleyball event, and the like. All such variations and examples would be covered in such example.

[0089] Returning to the present example, once the video input 218 is obtained by the computing device 200, in one example, the assessment unit 114 may cause to display the obtained video input 218 on the display device 232.

[0090] Thereafter, the extraction unit 210 may extract a set of successive time-stamped input images 220 from the video input 218. In an embodiment, the set of successive time-stamped images 220 may include an occurrence 224, which may be happening in the event 226. For example, the set of successive time-stamped images 220 may relate to an occurrence of a motion of the projectile, a movement of the participant or an interaction between the participant and the projectile.

[0091] Continuing further, each of the extracted input images 220 may include one or more entities 222. In one example, the one or more entities 222 is one of a projectile, a participant, and a combination thereof. In an embodiment, the projectile may be selected from a group consisting of a ball, a javelin, an arrow, a dart, a disc, a pellet, and combinations thereof. In an exemplary implementation, the projectile is a ball. In an embodiment, the participant refers to a player in the sporting event. In one example, the time-stamped input images 220 may include a plurality of participants of a plurality of projectiles.

[0092] The extraction of the one or more entities 222 includes identification of the projectile and the participant.

[0093] Once the one or more entities 222 are extracted from each of the successive time-stamped input images, thereafter, the analysis unit 212 is configured to analyse the set of successive input images 220 to determine one or more occurrences 224 in the video input 218.

[0094] In one example, the analysis refers to tracking of the position of the one or more entities 222 in the each of the successive time-stamped input images 220.

[0095] In another example, the analysis of the set of successive input images 220 includes determining a set of positions of the one or more entities 222 in each of the frames of successive time- stamped input images. Based on the determined set of positions, a movement of said one or more entities 222 in the successive time-stamped input images may be determined.

[0096] For example, the analysis of the successive input images 220 includes one of a determination of a trajectory of the projectile in the video input 218, determination of a movement of the participant in the video input 218, determination of one or more interactions between the projectile and the participant, and a combination thereof. The movement of the participant includes, but may not be limited to one of a resting stance, a moving stance, a stationary movement, a dynamic movement, and a combination thereof

[0097] Continuing further, once the one or more occurrences are determined in the event, the assessment unit 114 may then assess the one or more occurrences. Examples of such assessment of the one or more occurrences may include, but are not limited to, assessment of a trend of trajectory of one or more projectiles in the video input 218, determining variation between one or more trajectories of the one or more projectiles in the video input 218, determining one of a velocity, location, motion along a 2-dimensional (2D) plane, trajectory in 3 -dimensional (3D) plane, relative position, and a combination thereof, of the one or more projectiles, assessment of a trend movement patterns of the body of the participant, determination of variations between movement patterns of the body of theparticipant during different events, a determination of one of a posture, step size, swing motion of the arm of the participant, and a combination thereof.

[0098] As would be noted and appreciated, the approaches of the present subject matter allows analysis of the occurrences in the event, using any standard image capturing devices, unlike usage of specific LiDAR sensors used in conventional techniques.

[0099] For example, the present subject matter may assess the occurrences of the event to determine a trajectory in 3-dimensional (3D) plane across the ‘z’ axis, using a standard 2-dimensional (2D) camera.

[0100] For example, considering an example of assessment of a ball, which may be moving from a bowler’s hand to the batter. As the ball moves from the bowler's hand to the batter, it traces a path across all 3 Axis (X-height, Y- width and Z-depth). The video input 218, in the context of the present subject matter, in one example, may be captured using a front-on camera (placed on a tripod at the Bowling End of the Pitch) and at a resolution of 4K and 60 fps - i.e. to say that a 1.5 sec video (the approximate time that the bowl stays in motion within the Cricket Nets after its bowled) comprises around 90 frames. In each of these 90 frames, the ball exists in the X, Y and Z axis coordinates. However, conventionally, as the camera used is a regular camera (without Lidar), the actual frames captured are captured only in 2d form (i.e only in X and Y axis). However, for calculating data points such as Speed of the delivery and Pitch Map for the bowler, it is required to record the ball across all 3 Axis.

[0101] The present subject matter uses a standard 2D camera, and uses a technique that works on the data captured by the camera. The technique of the present subject matter analyzes the pixel data values in eCh frame of the cricket ball from the point of release (from the bowler’s hand) to the farthest point of its travel (either the impact with the batter’s bat, or if he misses or leaves it - the back end of the nets). Since the ball in essence moves away from the camera after being bowled and then ‘towards the camera’ if it is hit with the bat, from a 2D camera’s perspective, the relative size of the ball in each frame reduces - as it moves away and increases as it moves back towards the camera. This also results in the number of pixels that make up the ball, reducing in value as it moves towards the batter and increasing as it moves back after being hit. The said technique calculates and processes this increase / decrease and juxtaposes this data in real world meters and centimeters (across 22 yards). This allows us to create an approximation of the Z axis of the ball at any given frame in the ball’s entire journey after being released.

[0102] As would be further noted and appreciated, as the speed of the ball varies after its release, a regular front facing camera is often unable to accurately capture it in each and every frame - oftenleading to distortions of the image of the ball in certain frames. This distortion takes the form of the ball appearing to be ‘elongated’ in certain frames - which greatly distorts the pixel data recorded. Hence the technique, of the present subject matter, uses a form of ‘rolling averages’ system that ignores any sudden distortions in the flow of data that is being recorded across multiple frames.

[0103] However, it may be noted that the above-provided examples of the assessment of the one or more occurrences of the event are only exemplary, and in no manner is construed to limit the scope of the present subject matter in any manner. Once the analysis unit 212 has determined one or more occurrences in the event, the assessment unit 114 may assess any other parameters, variations, or trends in the determined occurrences. All such examples and variations would be covered within the scope of the present subject matter.

[0104] Continuing further, in furtherance to the assessment of the determined one or more occurrences, the assessment unit 114 is configured to cause to display the assessed one or more occurrences in the video input on the display device 232. In an embodiment, the display device 232 comprises a screen, a projector, an augmented reality (AR) device, a virtual reality (VR) device, a mixed reality (MR) device, or combinations thereof.

[0105] In an example, the assessment unit 114 is configured to collate the analysis of the one or more occurrences 224. In yet another example, the assessment may include a presentation of the analysis of the one or more occurrences 224. In yet another example, the assessment may be presented using one or more graphics, one or more texts, or a combination thereof. In yet another example, the assessment may be presented using one or more texts, audio, or a combination thereof.

[0106] In an additional implementation of the present disclosure, the computing device 200 may be communicatively coupled to an external database or a repository (not depicted in FIG. 2). In an example, the external database may be a distributed database, or a secure database. In yet another example, the external database may be a remote database or a cloud-based database.

[0107] In an example, the external database may include a plurality of datasets relating to interactions between reference participants and projectiles. In another example, the plurality of datasets includes a plurality of body movement features of the reference participants and the corresponding projectile trajectories after interaction with the reference participants and the projectiles. In yet another example, the reference participants are expert participants.

[0108] In yet another example, the datasets may be created by performing the assessment on the expert participants. The outcomes for a plurality of expert participants are stored in the external database as different datasets. In yet another example, the datasets are categorized according to anidentification of a specific reference participant. In yet another example, the datasets are categorized according to a specific feature pertaining to the occurrence.

[0109] In the context of the present example, in an example, based on the assessment of the one or more occurrences in the event, the assessment unit 114 is configured to provide an appropriate external reference dataset. In another example based on the entities and occurrences on which the assessment is based, the assessment unit 114 is configured to select, from the external database, an appropriate dataset including the extracted one or more entities 222 and occurrences 224 corelating to the assessment.

[0110] In an embodiment, based on a request of the user, the assessment unit 114 is configured to filter the external reference dataset. In an embodiment, the user request may include any one or more reference participants whose reference is to be provided. In an embodiment, the user may input the request at the interface. In an embodiment, the user may input the request during the stage of input of the input images. In an embodiment, the user may input the request at any period subsequent to input of the video input 218.

[0111] Thereafter, in another example, the display device 232 may be configured to display the assessment based on the retrieved external reference dataset.

[0112] FIG. 3 illustrates a flowchart depicting an example method 300 for assessing one or more occurrences in an event, to be implemented in an exemplary implementation of the present subject matter. The order in which the method 300 is described is not intended to be construed as a limitation, and any number of the described method blocks may be combined in any order to implement the aforementioned method, or an alternative method. Furthermore, method 300 may be implemented by processing resource or computing device(s) through any suitable hardware, non-transitory machine- readable instructions, or combination thereof.

[0113] It may also be understood that method 300 may be performed by programmed system 102 or computing device 200 as depicted in FIGS. 1 or 2, respectively. Furthermore, the method 300 may be executed based on instructions stored in non-transitory computer readable medium, as will be readily understood. The non-transitory computer readable medium may include, for example digital memories, magnetic storage media, such as one or more magnetic disks and magnetic tapes, hard drives, or optically readable digital data storage media. Although, the method 300 is described below with reference to the computing device 200 as described above, other suitable systems for the execution of these methods can also be utilized. Additionally, implementation of this method is not limited to such examples.

[0114] At block 302, a video input 218 may be obtained. In one example, the video corresponds to an event. Thereafter, at block 304, a set of successive time-stamped input images may be extracted based on the obtained video input 218. In one example, each of the extracted input images may include one or more entities 222. At block 306, one or more entities 222 may be extracted from each of the successive time-stamped input images. Thereafter, based on the extracted one or more entities 222, at block 308, the set of successive input images may be analysed to determine one or more occurrences in the video input. Hereinafter, at block 310, the determined one or more occurrences in the video input 218 may be assessed.

[0115] The present disclosure further discloses a non-transitory computer readable storage medium storing instructions for assessing one or more occurrences in an event. The instructions include executable code which, when executed by a processing resource of a system, causes the system to obtain a video input 218. The video input 218 corresponds to an event. Further, the instructions include executable code which, when executed, causes the system to extract a set of successive time- stamped input images from the video input 218. Further, the instructions include executable code which, when executed, causes the system to extract one or more entities 222 from each of the successive time-stamped input images. Further, the instructions include executable code which, when executed, causes the system to analyse the set of successive input images to determine one or more occurrences in the video input, based on the extracted one or more entities 222.

[0116] Although examples for the present disclosure have been described in language specific to structural features and / or methods, it should be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed and explained as examples of the present disclosure.

Claims

We Claim:

1. A method (300) for assessing one or more occurrences in an event, the method (300) comprising: obtaining (302) a video input, wherein the video corresponds to an event; based on the obtained video input, extracting (304) a set of successive time-stamped input images, and wherein each of the extracted input images comprises one or more entities; extracting (306) one or more entities from each of the successive time-stamped input images; based on the extracted one or more entities, analysing (308) the set of successive input images to determine one or more occurrences in the video input; and assessing (310) the determined one or more occurrences in the video input.

2. The method (300) as claimed in claim 1, wherein the event is a sporting event, wherein the sporting event comprises one of a cricket event, an archery event, a javelin throw event, and a dart event.

3. The method (300) as claimed in claim 1, further comprising obtaining the video input based on a user input.

4. The method (300) as claimed in claim 1, further comprising obtaining the video input from a repository.

5. The method (300) as claimed in claim 1, further comprising obtaining the video input from an image capturing device in real-time.

6. The method (300) as claimed in claim 1, further comprising causing to display the obtained video input on a display device.

7. The method (300) as claimed in claim 1 , wherein the entity is one of a projectile, a participant, and a combination thereof, and wherein the projectile is one of a ball, a javelin, an arrow, a dart, a disc, a pellet, and a combination thereof.

8. The method (300) as claimed in claim 1, wherein analysing the set of successive input images based on the extracted one or more entities comprises: determining a set of positions of the one or more entities in each of the frames of successive time-stamped input images; and based on the determined set of positions, determining a movement of said one or more entities in the successive time-stamped input images.

9. The method (300) as claimed in claims 7 or 8, wherein determining the movement of said one or more entities in the successive time-stamped input images comprises one of a: determination of a trajectory of the projectile in the video input; determination of a movement of the participant in the video input, wherein the movement of the participant comprises one of a resting stance, a moving stance, a stationary movement, a dynamic movement, and a combination thereof; determination of one or more interactions between the projectile and the participant; and a combination thereof.

10. The method (300) as claimed in claim 7, wherein assessing the determined one or more occurrences in the video input comprises one of: assessing a trend of trajectory of one or more projectiles in the video input; determining variation between one or more trajectories of the one or more projectiles in the video input; determining one of a velocity, location, motion along a 2-dimensional (2D) plane, trajectory in 3 -dimensional (3D) plane, relative position, and a combination thereof, of the one or more projectiles; assessing a trend of movement patterns of the body of the participant; determining variations between movement patterns of the body of the participant during different events; determining one of a posture, step size, swing motion of the arm of the participant, and a combination thereof; and a combination thereof.

11. The method (300) as claimed in claim 1, further comprising causing to display the assessed one or more occurrences in the video input on a display device.

12. A system (102) for assessing one or more occurrences in an event, the system (102) comprising: a processor (104); and an assessment unit (114) coupled to the processor (104), wherein the assessment unit (114) is to: obtain a video input, wherein the video corresponds to an event; based on the obtained video input, extract a set of successive time-stamped input images, and wherein each of the extracted input images comprises one or more entities; extract one or more entities from each of the successive time-stamped input images; based on the extracted one or more entities, analyse the set of successive input images to determine one or more occurrences in the video input; and assess the determined one or more occurrences in the video input.

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