# US 2026/0148522 A1 · Vehicle based threat launch detection using long wave infrared cameras > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0148522 A1 - Document ID: US20260148522A1 - Status: Published application - Kind: A1 - Application number: 18/956,912 - Filed: 2024-11-22 - Published: 2026-05-28 - Earliest claimed: 2024-11-22 - Assignees: BAE Systems Information and Electronic Systems Integration Inc. - Applicants: BAE Systems Information and Electronic Systems Integration Inc. - Inventors: Eric M. Louchard - Web page: https://patentgrep.com/patent/US20260148522A1 ## CPC - G06V 10/764: using classification, e.g. of video objects - G06T 1/20: Processor architectures; Processor configuration, e.g. pipelining - G06T 7/246: using feature-based methods, e.g. the tracking of corners or segments - G06T 7/97: {Determining parameters from multiple pictures (depth or shape recovery from multiple images G06T7/55; stereo camera calibration G06T7/85)} - G06V 10/82: using neural networks - G06V 20/52: Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) - G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle - G06T 2207/10016: Video; Image sequence - G06T 2207/10024: Color image - G06T 2207/10048: Infrared image - G06T 2207/20081: Training; Learning - G06T 2207/20084: Artificial neural networks [ANN] - G06T 2207/30212: Military - G06V 2201/07: Target detection - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A computer program product interacts with machine-readable mediums with instructions for automated launch detection and recognition. It captures a video of a specified region using detectors on a vehicle. Raw image frames from the video undergo pre-processing, followed by feeding through anomalous clutter tracker pipeline, exceedance detection pipeline using global clutter suppression pipeline and an optical flow pipeline. Image windows are generated by rejecting clutter using exceedance pixel locations to create image chips of the target and local background that are run through a convolutional neural network, classifying them as launches or background clutter. A Kalman filter is applied to at least one frame detection list to generate a launch detection list and causes the launch to be detected. ## Claims ### Claim 1 A computer program product including one or more non-transitory machine-readable mediums encoded with instructions encoded thereon that when executed by one or more processors cause a process to be carried out for automated launch detection and tracking, the instructions comprising: capture, via at least one detector, a sequence of image frames that depict a region of interest (ROI); process the sequence of image frames with an anomalous clutter tracker (ACT) pipeline, to detect at least one launch in the sequence of image frames; apply a machine learning technique to the sequence of image frames to classify the at least one launch therein; generate at least one frame detection list containing data about the at least one launch; and apply at least one clutter tracker filter to the at least one frame detection list to generate a detection list including the at least one launch, wherein the at least one launch is detected in response to a detection in a video of the ROI, and causing the at least one launch to be detected. ### Claim 2 (depends on 1) The computer program product of claim 1, the instructions further comprising: process the sequence of image frames through an exceedance detection (ED) pipeline using a global clutter suppression (GCS) routine and an optical flow pipeline (OFP); combine an output of the ACT pipeline with an output of the ED pipeline to enter a clutter rejection (CR) pipeline; and generate a frame array from each of the ACT and ED pipelines. ### Claim 3 (depends on 1) The computer program product of claim 1, the instructions further comprising: process raw image frames through a clutter rejection (CR) pipeline; filter a set of image windows from the raw image frames in the CR pipeline; and group the set of image windows into a batch and apply the machine learning technique to the batch to classify the at least one launch therein. ### Claim 4 (depends on 3) The computer program product of claim 3, the instructions further comprising: determine and filter cluster exceedances; create a batch of image chips from filtered cluster exceedances in the CR pipeline; and create a batch of non-redundant image chips from the ACT and GCS pipelines. ### Claim 5 (depends on 1) The computer program product of claim 1, the instructions further comprising: create detection and background windows; extract flow vectors from the detection and background windows; determine and apply a brightness threshold of a maximum intensity of pixels within the detection window to a mean intensity of pixels in the background window; and locally suppress clutter based on a signal to noise ratio threshold based on a target pixel intensity, background spectrum, and background standard deviation. ### Claim 6 (depends on 2) The computer program product of claim 2, the instructions further comprising: calculate initial flow vectors from the OFP, wherein the OFP comprises instructions to: enhance contrast of a test frame image; calculate pixel flow of the enhanced contrast test frame image; and create flow vector maps based on pixel flow. ### Claim 7 (depends on 1) The computer program product of claim 1, the instructions further comprising: extract a target spectrum with an updated flow adjustment; compare a generalized likelihood ratio test (“GLRT”) calculation target to a library of stored exceedance images; apply a GLRT threshold to the extracted target spectrum; filter matches to clutter spectra based on GLRT spectra; and filter detections on clutter tracks. ### Claim 8 (depends on 1) The computer program product of claim 1, wherein the instructions further comprise: populate a launch detection list. ### Claim 9 (depends on 1) The computer program product of claim 1, wherein the instructions further comprise: capture a video from at least two detectors, wherein a first director is a red green blue (RGB) video camera and a second detector is a long-wave infrared (LWIR) video camera. ### Claim 10 (depends on 1) The computer program product of claim 1, wherein the instructions further comprise: detect a first target with a RGB video camera; and detect a second target with LWIR video camera. ### Claim 11 A method of automated launch detection and tracking, the method comprising: capturing, via at least one detector, a sequence of image frames that define a video depicting a region of interest (ROI); processing the sequence of image frames with an anomalous clutter tracker (ACT) pipeline, to detect at least one launch in the sequence of image frames; applying a machine learning technique to the sequence of image frames to classify the at least one launch therein; generating at least one frame detection list containing data about the at least one launch; and applying at least one clutter tracker Kalman filter to the at least one frame detection list to generate a detection list including the at least one launch, wherein the at least one launch is detected in response to a detection in the video of the ROI, and causing the at least one launch to be detected. ### Claim 12 (depends on 11) The method of claim 11, further comprising: processing the sequence of image frames through an exceedance detection (ED) pipeline using a global clutter suppression (GCS) routine; and an optical flow pipeline (OFP); combining an output of the ACT pipeline with an output of the ED pipeline to enter a clutter rejection (CR) pipeline; and generating a frame array from each of the ACT and ED pipelines. ### Claim 13 (depends on 11) The method of claim 11, further comprising: processing raw image frames through a clutter rejection (CR) pipeline; filtering a set of image windows from the raw image frames in the CR pipeline; and grouping the set of image windows into a batch and applying the machine learning technique to the batch to classify the at least one launch therein. ### Claim 14 (depends on 11) The method of claim 11, further comprising: determining and filtering cluster exceedances; creating a batch of image chips from filtered cluster exceedances in the CR pipeline; and creating a batch of non-redundant image chips from the ACT and GCS pipelines. ### Claim 15 (depends on 11) The method of claim 11, further comprising: creating detection and background windows; extracting flow vectors from the detection and background windows; determining and applying a brightness threshold; and locally suppressing clutter based on a signal to noise ratio threshold. ### Claim 16 (depends on 12) The method of claim 12, further comprising: providing initial flow vectors from the OFP, wherein the OFP comprises instructions to: enhance contrast of a test frame image; calculate pixel flow of the test frame image; and create flow vector maps based on pixel flow. ### Claim 17 (depends on 11) The method of claim 11, further comprising: extracting a target spectrum with an updated flow adjustment; comparing a GLRT calculation target to a library of stored exceedance images; applying a GLRT threshold; filtering matches to clutter spectra based on GLRT spectra; and filtering detections on clutter tracks. ### Claim 18 (depends on 11) The method of claim 11, further comprising: populating a launch detection list. ### Claim 19 (depends on 11) The method of claim 11, further comprising: capturing a video from at least two detectors, wherein a first director is a RGB video camera and a second detector is a LWIR video camera. ### Claim 20 (depends on 11) The method of claim 11, further comprising: detecting a first target with a RGB video camera; and detecting a second target with LWIR camera.