An apparatus, comprising: a memory; and a processor operatively coupled to the memory, the processor configured to: receive a set of images (101 -101(n)) associated with a video recorded by a moving or a non-moving camera, detect a structure of a region of interest from a plurality of regions of interest in an image from the set of images, classify the structure into a geometric class from a set of predefined geometric classes defined using machine learning techniques, based on a machine learning model and a set of parameters stored in the memory, alter the region of interest to generate an altered image when the geometric class is associated with an identity of a person, such that privacy associated with the identity of the person is protected, and at least one of send the altered image to a user interface or store the altered image in a standardized format, wherein the geometric class is associated with a background and not associated with the identity of the person when the structure of the region of interest includes a set of substantially straight lines, wherein the processor is further configured to: define a grid placed over the set of images, the grid including a plurality of grid cells, each grid cell from the plurality of grid cells associated with a different region of interest from the plurality of regions of interest in the image, detect a structure of each region of interest in each grid cell from the plurality of grid cells, and classify each grid cell into the set of predefined geometric classes using the set of parameters stored in the memory. a memory; and a processor operatively coupled to the memory, the processor configured to: receive a set of images (101 -101(n)) associated with a video recorded by a moving or a non-moving camera, detect a structure of a region of interest from a plurality of regions of interest in an image from the set of images, classify the structure into a geometric class from a set of predefined geometric classes defined using machine learning techniques, based on a machine learning model and a set of parameters stored in the memory, alter the region of interest to generate an altered image when the geometric class is associated with an identity of a person, such that privacy associated with the identity of the person is protected, and at least one of send the altered image to a user interface or store the altered image in a standardized format, wherein the geometric class is associated with a background and not associated with the identity of the person when the structure of the region of interest includes a set of substantially straight lines, wherein the processor is further configured to: define a grid placed over the set of images, the grid including a plurality of grid cells, each grid cell from the plurality of grid cells associated with a different region of interest from the plurality of regions of interest in the image, detect a structure of each region of interest in each grid cell from the plurality of grid cells, and classify each grid cell into the set of predefined geometric classes using the set of parameters stored in the memory. receive a set of images (101 -101(n)) associated with a video recorded by a moving or a non-moving camera, detect a structure of a region of interest from a plurality of regions of interest in an image from the set of images, classify the structure into a geometric class from a set of predefined geometric classes defined using machine learning techniques, based on a machine learning model and a set of parameters stored in the memory, alter the region of interest to generate an altered image when the geometric class is associated with an identity of a person, such that privacy associated with the identity of the person is protected, and at least one of send the altered image to a user interface or store the altered image in a standardized format, wherein the geometric class is associated with a background and not associated with the identity of the person when the structure of the region of interest includes a set of substantially straight lines, wherein the processor is further configured to: define a grid placed over the set of images, the grid including a plurality of grid cells, each grid cell from the plurality of grid cells associated with a different region of interest from the plurality of regions of interest in the image, detect a structure of each region of interest in each grid cell from the plurality of grid cells, and classify each grid cell into the set of predefined geometric classes using the set of parameters stored in the memory. define a grid placed over the set of images, the grid including a plurality of grid cells, each grid cell from the plurality of grid cells associated with a different region of interest from the plurality of regions of interest in the image, detect a structure of each region of interest in each grid cell from the plurality of grid cells, and classify each grid cell into the set of predefined geometric classes using the set of parameters stored in the memory.