# US 2025/0095366 A1 · Automated metadata generation from full motion video > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0095366 A1 - Document ID: US20250095366A1 - Status: Published application - Kind: A1 - Application number: 18/887,030 - Filed: 2024-09-16 - Published: 2025-03-20 - Earliest claimed: 2023-09-18 - Assignees: Tactiql Inc. - Applicants: Tactiql Inc. - Inventors: Michael NELSON; Rasha KASHEF; Liam MARTIN; Hrag JEBAMIKYOUS; Mustafa ALJASIM - Web page: https://patentgrep.com/patent/US20250095366A1 ## CPC - G06V 20/46: {Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames} - G06V 20/17: taken from planes or by drones - G06V 20/635: {Overlay text, e.g. embedded captions in a TV programme} - G06V 10/82: using neural networks - G06V 2201/10: Recognition assisted with metadata - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A system and method for generating metadata to accompany motion imagery data captured by an Unmanned Aerial System or similar platform extracts heads-up display content from the video content of the motion imagery data and correlates the extracted content with at least one metadata field to provide extracted metadata. The extracted metadata may be supplemented with synchronous metadata included in the motion imagery data, if any is available. Camera footprint coordinates are then computed using the extracted and optionally the supplementary metadata. Computation of the camera footprint coordinates may include simulating metadata such as sensor coordinates, timestamp, altitude, and/or heading angle, or deriving further metadata such as speed and rates of change of altitude and heading angle. ## Claims ### Claim 1 A computer-implemented method for synthesizing metadata from motion imagery, comprising: receiving, by one or more processors, input motion imagery data comprising video content captured by an Unmanned Aerial System (UAS) platform and including text overlaid over the captured video content; extracting, by the one or more processors, at least a portion of the text from the video content; correlating, by the one or more processors, the extracted text data with at least one metadata field to provide extracted metadata; and storing the extracted metadata associated with the motion imagery data. ### Claim 2 (depends on 1) The method of claim 1, wherein the extracted metadata comprises geographic coordinates for a position associated with a camera used to capture the video content. ### Claim 3 (depends on 2) The method of claim 2, wherein the geographic coordinates comprise geographic coordinates for a center of a field of view of the camera. ### Claim 4 (depends on 1) The method of claim 1, wherein the extracted metadata supplements metadata received with the motion imagery data. ### Claim 5 (depends on 4) The method of claim 4, wherein supplementing the extracted metadata comprises supplementing the extracted metadata with simulated metadata, the simulated metadata being determined from specifications of the UAS platform, sample video data for the UAS platform, and/or one or more machine learning models receiving extracted metadata or metadata derived from the extracted metadata as input. ### Claim 6 (depends on 5) The method of claim 5, wherein the extracted metadata comprises sensor frame coordinates or frame sensor coordinates, timestamp, altitude, and heading angle, and metadata derived from the extracted metadata comprises one or more of a speed, a rate of change of altitude, or a rate of change of heading angle. ### Claim 7 (depends on 6) The method of claim 6, wherein the simulated data comprises a pitch angle, the method further comprising determining the pitch angle using a machine learning model with the speed and the rate of change of altitude as inputs. ### Claim 8 (depends on 6) The method of claim 6, wherein the simulated data comprises a roll angle, the method further comprising determining the roll angle using a machine learning model with the speed and the rate of change of heading angle as inputs. ### Claim 9 (depends on 1) The method of claim 1, further comprising computing a camera footprint using at least the extracted metadata, wherein computing the camera footprint comprises deriving camera footprint coordinates for at least one frame or timestamp of the motion imagery data. ### Claim 10 (depends on 1) The method of claim 1, wherein a contrast level between the overlaid text and the captured video content varies over time. ### Claim 11 A computer system, comprising: at least one communications subsystem; memory; and at least one processor in operative communication with the at least one communications subsystem and memory, the at least one processor being configured to: receive input motion imagery data comprising video content captured by an Unmanned Aerial System (UAS) platform and including text overlaid over the captured video content; extract at least a portion of the text from the video content; correlate the extracted text data with at least one metadata field to provide extracted metadata; and store the extracted metadata associated with the motion imagery data. ### Claim 12 (depends on 11) The computer system of claim 11, wherein the extracted metadata comprises geographic coordinates for a position associated with a camera used to capture the video content. ### Claim 13 (depends on 12) The computer system of claim 12, wherein the geographic coordinates comprise geographic coordinates for a center of a field of view of the camera. ### Claim 14 (depends on 11) The computer system of claim 11, wherein the extracted metadata supplements metadata received with the motion imagery data. ### Claim 15 (depends on 11) The computer system of claim 11, wherein the at least one processor is configured to supplement the extracted metadata with simulated metadata, the simulated metadata being determined from specifications of the UAS platform, sample video data for the UAS platform, and/or one or more machine learning models receiving extracted metadata or metadata derived from the extracted metadata as input. ### Claim 16 (depends on 15) The computer system of claim 15, wherein the extracted metadata comprises sensor frame coordinates or frame sensor coordinates, timestamp, altitude, and heading angle, and metadata derived from the extracted metadata comprises one or more of a speed, a rate of change of altitude, or a rate of change of heading angle. ### Claim 17 (depends on 16) The computer system of claim 16, wherein the simulated data comprises a pitch angle, the at least one processor being configured to determine the pitch angle using a machine learning model with the speed and the rate of change of altitude as inputs. ### Claim 18 (depends on 16) The computer system of claim 16, wherein the simulated data comprises a roll angle, the at least one processor being configured to determine the roll angle using a machine learning model with the speed and the rate of change of heading angle as inputs. ### Claim 19 (depends on 11) The computer system of claim 11, wherein the at least one processor is further configured to compute a camera footprint using at least the extracted metadata, including deriving camera footprint coordinates for at least one frame or timestamp of the motion imagery data. ### Claim 20 (depends on 11) The computer system of claim 11, wherein a contrast level between the overlaid text and the captured video content varies over time.