Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:

METHOD AND SYSTEM FOR COLOR OVERLAY DETECTION AND CORRECTION

Applicant

Tata Consultancy Services Limited
A company Incorporated in India under the Companies Act, 1956
Having address:
Nirmal Building, 9th floor,
Nariman point, Mumbai 400021,
Maharashtra, India

Preamble to the description:

The following specification particularly describes the invention and the manner in which it is to be performed.
TECHNICAL FIELD
[001] The disclosure herein generally relates to video processing, and, more particularly, to a method and system for color overlay detection and correction.

BACKGROUND
[002] Video analytics serves as the backbone for most camera-based analytics systems. In such systems, the video quality plays a pivotal role in determining the success of the computer vision algorithms. Thus, it is essential that the quality of the image information is retained at best. However, due to different factors (e.g., lighting conditions, camera malfunctioning etc.) color overlay phenomena get introduced and it hinders the data analytics process. The color overlays in turn increases analytical complexity of the video processing systems while trying to detect, track and recognize objects. Also due to the presence of color overlay, undesired pixel distribution change take place in the scene.
[003] Similarly, from machine learning perspective, in order to train data models for any image analytics pipeline, quality training data is required, i.e., accuracy of such data models depends upon the quality of the training images. The data models are trained with annotated dataset extracted from existing video data and then validated with unseen data of same category. But, due to the presence of unwanted color overlay in the real time data, such models fail to provide accurate results. Also, the existing approaches mostly depend on manual filtration of the training data to discard images having unwanted color overlays, which is a tedious and error-prone job. Additionally, the train dataset size gets reduced as well.

SUMMARY
[004] Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one embodiment, a processor implemented method is provided. The method includes receiving, via one or more hardware processors, a plurality of image frames as input, wherein the plurality of image frames comprises a ground truth frame and a plurality of subsequent frames. Further, the ground truth frame is appended to a base bin, via the one or more hardware processors. Further, each of the plurality of subsequent frames is processed via the one or more hardware processors, wherein processing of each of the plurality of subsequent frames includes the following steps. Initially, a plurality of histograms are calculated, separately for a plurality of frames in the base bin at a current instance of time. Further, a histogram for the subsequent frame being processed at the current instance of time is calculated. Further, a similarity coefficient is determined separately between each of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame, wherein the similarity coefficient is a measure of similarity between each of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame. Further, an average of the similarity coefficients of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame is computed. Further, the computed average of the similarity coefficients is compared with a heuristically generated threshold of similarity coefficient, wherein the computed average of the similarity coefficients exceeding the threshold of similarity coefficient indicates presence of color overlay in the subsequent frame being processed.
[005] In an embodiment of the method, the determined presence of color overlay is corrected using a histogram equalization based technique to generate an associated clean frame, comprising normalizing pixel distribution of the subsequent frame in which the presence of color overlay has been determined, by matching the histogram of the subsequent frame with a histogram of the ground truth frame.
[006] In an embodiment of the method, by matching the histogram of the subsequent frame with the histogram of the ground truth frame, a color pixel distribution of the ground truth frame is introduced in the subsequent frame in which the presence of color overlay has been determined.
[007] In an embodiment of the method, a final image representation is generated by appending the clean frame at each of a plurality of time instances to the base bin, till the base bin is full, wherein if the base bin is full, a frame having least value of the similarity coefficients from among a plurality of frames in the base bin is removed and is replaced with a frame having a higher value of the similarity coefficient.
[008] In yet another embodiment, a system is provided. The system includes one or more hardware processors, a communication interface, and a memory storing a plurality of instructions. The plurality of instructions cause the one or more hardware processors to receive a plurality of image frames as input, wherein the plurality of image frames comprises a ground truth frame and a plurality of subsequent frames. Further, the ground truth frame is appended to a base bin, via the one or more hardware processors. Further, each of the plurality of subsequent frames is processed via the one or more hardware processors, wherein processing of each of the plurality of subsequent frames includes the following steps. Initially, a plurality of histograms are calculated, separately for a plurality of frames in the base bin at a current instance of time. Further, a histogram for the subsequent frame being processed at the current instance of time is calculated. Further, a similarity coefficient is determined separately between each of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame, wherein the similarity coefficient is a measure of similarity between each of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame. Further, an average of the similarity coefficients of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame is computed. Further, the computed average of the similarity coefficients is compared with a heuristically generated threshold of similarity coefficient, wherein the computed average of the similarity coefficients exceeding the threshold of similarity coefficient indicates presence of color overlay in the subsequent frame being processed.
[009] In an embodiment of the system, the one or more hardware processors are configured to correct the determined presence of color overlay using a histogram equalization based technique to generate an associated clean frame, including normalizing pixel distribution of the subsequent frame in which the presence of color overlay has been determined, by matching the histogram of the subsequent frame with a histogram of the ground truth frame.
[010] In an embodiment of the system, by matching the histogram of the subsequent frame with the histogram of the ground truth frame, a color pixel distribution of the ground truth frame is introduced in the subsequent frame in which the presence of color overlay has been determined.
[011] In an embodiment of the system, a final image representation is generated by appending the clean frame at each of a plurality of time instances to the base bin, till the base bin is full, wherein if the base bin is full, a frame having least value of the similarity coefficients from among a plurality of frames in the base bin is removed and is replaced with a frame having a higher value of the similarity coefficient.
[012] In yet another aspect, a non-transitory computer readable medium is provided. The non-transitory computer readable medium includes a plurality of instructions, which when executed, causes one or more hardware processors to receive a plurality of image frames as input, wherein the plurality of image frames comprises a ground truth frame and a plurality of subsequent frames. Further, the ground truth frame is appended to a base bin, via the one or more hardware processors. Further, each of the plurality of subsequent frames is processed via the one or more hardware processors, wherein processing of each of the plurality of subsequent frames includes the following steps. Initially, a plurality of histograms are calculated, separately for a plurality of frames in the base bin at a current instance of time. Further, a histogram for the subsequent frame being processed at the current instance of time is calculated. Further, a similarity coefficient is determined separately between each of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame, wherein the similarity coefficient is a measure of similarity between each of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame. Further, an average of the similarity coefficients of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame is computed. Further, the computed average of the similarity coefficients is compared with a heuristically generated threshold of similarity coefficient, wherein the computed average of the similarity coefficients exceeding the threshold of similarity coefficient indicates presence of color overlay in the subsequent frame being processed.
[013] In yet another embodiment of the non-transitory computer readable medium, the determined presence of color overlay is corrected using a histogram equalization based technique to generate an associated clean frame, comprising normalizing pixel distribution of the subsequent frame in which the presence of color overlay has been determined, by matching the histogram of the subsequent frame with a histogram of the ground truth frame.
[014] In yet another embodiment of the non-transitory computer readable medium, by matching the histogram of the subsequent frame with the histogram of the ground truth frame, a color pixel distribution of the ground truth frame is introduced in the subsequent frame in which the presence of color overlay has been determined.
[015] In yet another embodiment of the non-transitory computer readable medium, a final image representation is generated by appending the clean frame at each of a plurality of time instances to the base bin, till the base bin is full, wherein if the base bin is full, a frame having least value of the similarity coefficients from among a plurality of frames in the base bin is removed and is replaced with a frame having a higher value of the similarity coefficient.
[016] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS
[017] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles:
[018] FIG. 1 illustrates an exemplary system for color overlay detection and correction, according to some embodiments of the present disclosure.
[019] FIG. 2 is a flow diagram depicting steps involved in the process of the color overlay detection and correction being performed by the system of FIG. 1, according to some embodiments of the present disclosure.
[020] FIG. 3 illustrates example images associated with the color overlay detection and correction, in accordance with some embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS
[021] Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the scope of the disclosed embodiments.
[022] Due to different factors (e.g., lighting conditions, camera malfunctioning, and so on) color overlay phenomena effects and hinders the image/video data analytics process.. The color overlays in turn increases analytical complexity of video processing systems while trying to detect, track and recognize objects. Also due to the presence of color overlay, undesired pixel distribution change takes place in the scene. Similarly, from a machine learning perspective, in order to train data models for such color overlay detection, quality training data is required, i.e., accuracy of such data models depends upon the quality of the training images. The data models are trained with annotated dataset extracted from existing video data and then validated with unseen data of same category. But, due to the presence of unwanted color overlay in the real time data, such models fail to provide accurate results. Also, the existing approaches mostly depend on manual filtration of the training data to discard images having unwanted color overlays, which is a tedious and error-prone job. Additionally, the train dataset size gets reduced as well.
[023] In order to address these challenges, the method and system disclosed herein provide a mechanism for color overlay detection and correction. The method includes receiving a plurality of image frames as input, wherein the plurality of image frames comprises a ground truth frame and a plurality of subsequent frames. Further, the ground truth frame is appended to a base bin. Further, each of the plurality of subsequent frames is processed, wherein processing of each of the plurality of subsequent frames includes the following steps. Initially, a plurality of histograms are calculated, separately for a plurality of frames in the base bin at a current instance of time. Further, a histogram for the subsequent frame being processed at the current instance of time is calculated. Further, a similarity coefficient is determined separately between each of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame, wherein the similarity coefficient is a measure of similarity between each of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame. Further, an average of the similarity coefficients of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame is computed. Further, the computed average of the similarity coefficients is compared with a threshold of similarity coefficient, wherein the computed average of the similarity coefficients exceeding the threshold of similarity coefficient indicates presence of color overlay in the subsequent frame being processed. The determined presence of color overlay is then corrected using a histogram equalization based technique to generate an associated clean frame, comprising normalizing pixel distribution of the subsequent frame in which the presence of color overlay has been determined, by matching the histogram of the subsequent frame with a histogram of the ground truth frame.
[024] Referring now to the drawings, and more particularly to FIG. 1 through FIG. 3, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.
[025] FIG. 1 illustrates an exemplary system 100 for color overlay detection and correction, according to some embodiments of the present disclosure. The system 100 includes or is otherwise in communication with hardware processors 102, at least one memory such as a memory 104, an I/O interface 112. The hardware processors 102, memory 104, and the Input /Output (I/O) interface 112 may be coupled by a system bus such as a system bus 108 or a similar mechanism. In an embodiment, the hardware processors 102 can be one or more hardware processors.
[026] The I/O interface 112 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 112 may include a variety of software and hardware interfaces, for example, interfaces for peripheral device(s), such as a keyboard, a mouse, an external memory, a printer and the like. Further, the I/O interface 112 may enable the system 100 to communicate with other devices, such as web servers, and external databases.
[027] The I/O interface 112 can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, local area network (LAN), cable, etc., and wireless networks, such as Wireless LAN (WLAN), cellular, or satellite. For the purpose, the I/O interface 112 may include one or more ports for connecting several computing systems with one another or to another server computer. The I/O interface 112 may include one or more ports for connecting several devices to one another or to another server.
[028] The one or more hardware processors 102 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, node machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the one or more hardware processors 102 is configured to fetch and execute computer-readable instructions stored in the memory 104.
[029] The memory 104 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, the memory 104 includes a plurality of modules 106.
[030] The plurality of modules 106 include programs or coded instructions that supplement applications or functions performed by the system 100 for executing different steps involved in the process of the color overlay detection and correction, being performed by the system 100. The plurality of modules 106, amongst other things, can include routines, programs, objects, components, and data structures, which performs particular tasks or implement particular abstract data types. The plurality of modules 106 may also be used as, signal processor(s), node machine(s), logic circuitries, and/or any other device or component that manipulates signals based on operational instructions. Further, the plurality of modules 106 can be used by hardware, by computer-readable instructions executed by the one or more hardware processors 102, or by a combination thereof. The plurality of modules 106 can include various sub-modules (not shown). The plurality of modules 106 may include computer-readable instructions that supplement applications or functions performed by the system 100 for the color overlay detection and correction.
[031] The data repository (or repository) 110 may include a plurality of abstracted piece of code for refinement and data that is processed, received, or generated as a result of the execution of the plurality of modules in the module(s) 106.
[032] Although the data repository 110 is shown internal to the system 100, it will be noted that, in alternate embodiments, the data repository 110 can also be implemented external to the system 100, where the data repository 110 may be stored within a database (repository 110) communicatively coupled to the system 100. The data contained within such external database may be periodically updated. For example, new data may be added into the database (not shown in FIG. 1) and/or existing data may be modified and/or non-useful data may be deleted from the database. In one example, the data may be stored in an external system, such as a Lightweight Directory Access Protocol (LDAP) directory and a Relational Database Management System (RDBMS). Functions of the components of the system 100 are now explained with reference to the steps in flow diagrams in FIG. 2, and the example diagram in FIG. 3.
[033] FIG. 2 is a flow diagram depicting steps involved in the process of the color overlay detection and correction being performed by the system of FIG. 1, according to some embodiments of the present disclosure. In an embodiment, the system 100 comprises one or more data storage devices or the memory 104 operatively coupled to the processor(s) 102 and is configured to store instructions for execution of steps of a method 200 by the processor(s) or one or more hardware processors 102. The steps of the method 200 of the present disclosure will now be explained with reference to the components or blocks of the system 100 as depicted in FIG. 1 and the steps of flow diagram as depicted in FIG. 2. Although process steps, method steps, techniques or the like may be described in a sequential order, such processes, methods, and techniques may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described does not necessarily indicate a requirement that the steps to be performed in that order. The steps of processes described herein may be performed in any order practical. Further, some steps may be performed simultaneously.
[034] At step 202 of a method 200 in FIG. 2, the system 100 receives, via the one or more hardware processors 102, a plurality of image frames as input. The plurality of image frames includes a ground truth frame and a plurality of subsequent frames. The plurality of image frames may correspond to a video being analyzed by the system 100, for the color overlay detection and correction.
[035] Further, at step 204 of the method 200, the system 100 appends the ground truth frame to a base bin, via the one or more hardware processors 102. Further, at step 206, the system 100 processes each of the plurality of subsequent frames, via the one or more hardware processors 102. Processing of each of the plurality of subsequent frames includes the steps 206a through 206e, which are explained hereafter.
[036] At step 206a, the system 100 calculates a plurality of histograms, separately for a plurality of frames in the base bin at a current instance of time. Further, at step 206b, the system 100 calculates a histogram for the subsequent frame being processed at the current instance of time is. The computed histograms may be any dataset represented in a frequency of the occurrences of the values in the dataset in a certain range. To compute the histograms, the system 100 initially converts the frame being processed (i.e., the frames in the base bin and/or the subsequent frames) from a Blue-Green-Red (BGR) color space to a Hue-Saturation-Value (HSV) color space, so as to improve component distribution. Further, from the HSV color space, Hue (H) and Saturation (S) spaces are selected as these values are very sensitive to the changes present in the scene, whereas Value (V) space is less impacted. Further, the histogram is computed based on the selected ranges of H and S scales for heuristically computed sizes.
[037] Further, at step 206c, the system 100 determines a similarity coefficient, separately between each of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frames. The similarity coefficient is a measure of similarity between each of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame. The system 100 may be configured to use any suitable technique for determining the similarity coefficient value. For example, a Bhattacharya distance (BDist) based approach is used. The BDist is generally used to calculate the similarity between two probability distributions which is a standard concept and can be directly applied to any conventional distributions. However, BDist cannot be directly applied to the color scale distributions which are not generic histograms. To make the BDist suitable for determining the similarity coefficient between the histograms, in which a custom-made distribution is generated from a frame, based on selective HSV color space components. The system 100 uses the BDist to understand correlation between custom-computed distributions. For two different frames, the similarity between their respective custom-computed distributions is computed using the BDist and it is used to determine nature of the frame. In an embodiment, the system 100 may use any suitable approach, i.e., other than the BDist approach, for determining the similarity coefficient value. The embodiments disclosed herein are not to be construed as limiting to use of BDist approach alone.
[038] Further, at step 206d, the system 100 computes an average of the similarity coefficients of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame. Further, at step 206e, the system 100 compares the computed average of the similarity coefficients with a threshold of similarity coefficient. Value of the threshold of similarity coefficient maybe pre-configured or dynamically configured by any authorized personnel, using appropriate interface provided. The system 100 is configured to consider value of the computed average of the similarity coefficients exceeding the threshold of similarity coefficient as indicative of presence of color overlay in the subsequent frame being processed.
[039] Further, the system 100 processes each frame in which the presence of color overlay is detected, to as to perform color overlay correction. For the color overlay correction for a frame, and to generate corresponding clean frame, the system 100 uses a histogram equalization based technique. In this technique, the system 100 normalizes pixel distribution of the subsequent frame in which the presence of color overlay has been determined, by matching the histogram of the subsequent frame with a histogram of the ground truth frame, thus attaining the color overlay correction. While matching the histogram of the subsequent frame with the histogram of the ground truth frame, a color pixel distribution of the ground truth frame is introduced in the subsequent frame in which the presence of color overlay has been determined.
[040] After correcting all the color overlayed frames, the system 100 generates a final corrected image representation. In order to generate the final image representation, the system 100 uses all clean frames which includes the clean frames obtained after the color overlay correction, and the frames in which the presence of color overlay is not detected at step 206 of the method 200. In an embodiment, the system 100 calculates the similarity coefficient for the frames in which the presence of color overlay was not detected at step 206. Further, in the process of generating the final image representation, the system 100 appends the clean frame at each of a plurality of time instances to the base bin. The base bin has a maximum capacity or limit and can be easily configured as per the interest of the problem statement The system 100 appends the clean frames to the base bin till the base bin is full. When the base bin is full, i.e., the maximum capacity has reached, the system 100 removes a frame having least value of the similarity coefficients from among a plurality of frames in the base bin, and replaces it with a frame having a higher value of the similarity coefficient. This process is repeated till all the clean frames are processed and added to the bin, based on the value of respective similarity score. As depicted in the example figure in FIG. 3, when a clean image was fed to the system 100, the system 100 detected it as clean image, whereas when the clean image was overlayed with red and yellow colors respectively and was fed as input, the system 100 detected the color overlay and corrected the images to generate corresponding clean images.
[041] The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
[042] The embodiments of present disclosure herein address unresolved problem of color overlay detection and correction in video processing. The embodiment thus provides a mechanism for color overlay detection using a custom computed histogram based approach. Moreover, the embodiments herein further provide a dynamic bin based mechanism for correction of the color overlay and for generating a final image representation.
[043] It is to be understood that the scope of the protection is extended to such a program and in addition to a computer-readable means having a message therein; such computer-readable storage means contain program-code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The hardware device can be any kind of device which can be programmed including e.g., any kind of computer like a server or a personal computer, or the like, or any combination thereof. The device may also include means which could be e.g., hardware means like e.g., an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or a combination of hardware and software means, e.g., an ASIC and an FPGA, or at least one microprocessor and at least one memory with software processing components located therein. Thus, the means can include both hardware means and software means. The method embodiments described herein could be implemented in hardware and software. The device may also include software means. Alternatively, the embodiments may be implemented on different hardware devices, e.g., using a plurality of CPUs.
[044] The embodiments herein can comprise hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, etc. The functions performed by various components described herein may be implemented in other components or combinations of other components. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can comprise, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
[045] The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
[046] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.
[047] It is intended that the disclosure and examples be considered as exemplary only, with a true scope of disclosed embodiments being indicated by the following claims.
, Claims:We Claim:
1. A processor implemented method (200), comprising:
receiving (202), via one or more hardware processors, a plurality of image frames as input, wherein the plurality of image frames comprises a ground truth frame and a plurality of subsequent frames;
appending (204), via the one or more hardware processors, the ground truth frame to a base bin;
processing (206), via the one or more hardware processors, each of the plurality of subsequent frames, comprising:
calculating (206a) a plurality of histograms, separately for a plurality of frames in the base bin at a current instance of time;
calculating (206b) a histogram for the subsequent frame being processed at the current instance of time;
determining (206c) a similarity coefficient separately between each of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame, wherein the similarity coefficient is a measure of similarity between each of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame;
computing (206d) an average of the similarity coefficients of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame; and
comparing (206e) the computed average of the similarity coefficients with a threshold of similarity coefficient, wherein the computed average of the similarity coefficients exceeding the threshold of similarity coefficient indicates presence of color overlay in the subsequent frame being processed.

2. The method as claimed in claim 1, comprising correcting the determined presence of color overlay using a histogram equalization based technique to generate an associated clean frame, comprising normalizing pixel distribution of the subsequent frame in which the presence of color overlay has been determined, by matching the histogram of the subsequent frame with a histogram of the ground truth frame.

3. The method as claimed in claim 2, wherein by matching the histogram of the subsequent frame with the histogram of the ground truth frame, a color pixel distribution of the ground truth frame is introduced in the subsequent frame in which the presence of color overlay has been determined.

4. The method as claimed in claim 2 comprising generating a final image representation, by appending the clean frame at each of a plurality of time instances to the base bin, till the base bin is full, wherein if the base bin is full, a frame having least value of the similarity coefficients from among the plurality of frames in the base bin is removed and is replaced with a frame having a higher value of the similarity coefficient.

5. A system (100), comprising:
one or more hardware processors (102);
a communication interface (112); and
a memory (104) storing a plurality of instructions, wherein the plurality of instructions cause the one or more hardware processors to:
receive a plurality of image frames as input, wherein the plurality of image frames comprises a ground truth frame and a plurality of subsequent frames;
append the ground truth frame to a base bin;
process each of the plurality of subsequent frames, by:
calculating a plurality of histograms, separately for a plurality of frames in the base bin at a current instance of time;
calculating a histogram for the subsequent frame being processed at the current instance of time;
determining a similarity coefficient separately between each of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame, wherein the similarity coefficient is a measure of similarity between each of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame;
computing an average of the similarity coefficients of the plurality of histograms of the plurality of the frames in the base bin and the histogram of the subsequent frame; and
comparing the computed average of the similarity coefficients with a threshold of similarity coefficient, wherein the computed average of the similarity coefficients exceeding the threshold of similarity coefficient indicates presence of color overlay in the subsequent frame being processed.

6. The system as claimed in claim 5, wherein the one or more hardware processors are configured to correct the determined presence of color overlay using a histogram equalization based technique to generate an associated clean frame, comprising normalizing pixel distribution of the subsequent frame in which the presence of color overlay has been determined, by matching the histogram of the subsequent frames with a histogram of the ground truth frame.

7. The system as claimed in claim 6, wherein by matching the histogram of the subsequent frame with the histogram of the ground truth frame, a color pixel distribution of the ground truth frame is introduced in the subsequent frame in which the presence of color overlay has been determined.

8. The system as claimed in claim 6, wherein the one or more hardware processors are configured to generate a final image representation by appending the clean frame at each of a plurality of time instances to the base bin, till the base bin is full, wherein if the base bin is full, a frame having least value of the similarity coefficients from among a plurality of frames in the base bin is removed and is replaced with a frame having a higher value of the similarity coefficient.

Dated this 21st Day of September 2023

Tata Consultancy Services Limited
By their Agent & Attorney

(Adheesh Nargolkar)
of Khaitan & Co
Reg No IN-PA-1086