# US 2019/0244371 A1 · Target Detection, Tracking, and Classification in Compressive Measurement Domain > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0244371 A1 - Document ID: US20190244371A1 - Status: Published application - Kind: A1 - Application number: 15/888,044 - Filed: 2018-02-04 - Published: 2019-08-08 - Earliest claimed: 2018-02-04 - Assignees: Applied Research, LLC - Applicants: Applied Research, LLC - Inventors: CHIMAN KWAN - Web page: https://patentgrep.com/patent/US20190244371A1 ## CPC - G06V 10/255: {Detecting or recognising potential candidate objects based on visual cues, e.g. shapes} - G06F 18/2136: based on sparsity criteria, e.g. with an overcomplete basis - G06F 18/2415: based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate - G06T 7/251: {involving models} - G06T 7/262: using transform domain methods, e.g. Fourier domain methods - G06V 10/431: {Frequency domain transformation; Autocorrelation} - G06V 10/7715: {Feature extraction, e.g. by transforming the feature space, e.g. multi-dimensional scaling [MDS]; Mappings, e.g. subspace methods} - H04N 5/144: {Movement detection (for video coding H04N19/503; analysis of motion in general G06T7/20)} - H04N 19/132: Sampling, masking or truncation of coding units, e.g. adaptive resampling, frame skipping, frame interpolation or high-frequency transform coefficient masking - G06T 2207/10016: Video; Image sequence - G06T 2207/20056: Discrete and fast Fourier transform, [DFT, FFT] - G06T 2207/20076: Probabilistic image processing - G06V 2201/07: Target detection - G06V 2201/08: Detecting or categorising vehicles - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## Abstract The present invention is to provide a method and system using compressed data directly for target tracking and target classification in videos. The present invention uses a video imager to generate compressive measurements, and a random subsampling operator to compress the video data. It uses a Gaussian Mixture Model (GMM) for target detection and manual location of the target and putting a bounding box around the targets in the first frame is not required. It further applies a saliency-based algorithm to re-center the captured target. This re-centering process can be repeated multiple times and each application of re-centering will improve over the previous one. A pixel completion algorithm is used to fill in the missing pixels for the captured target area. A Sparse Representation Classification (SRC) for target classification. Both the target templates in a dictionary and captured targets are transformed to the frequency domain using Fast Fourier Transform (FFT). ## Claims ### Claim 1 A system for target detection, tracking and classification directly in a compressive measurement domain, comprising: an Imager having a Motion Detector (MD) connected to a Saliency Based Re-centering (SBR) generator for outputting a target location; a Missing Data Completion (MDC) generator connected to the output of the SBR generator; output of the MDC generator is connected to a Target Classification (TC) generator; a random subsampling operator to compress video data; and the output of the TC generator is connected to a Voting Based Scheme (VBS) generator for generating a target type of an image. ### Claim 2 (depends on 1) A system for target detection, tracking and classification directly in a compressive measurement domain in accordance to claim 1, further comprising: a Fast Fourier Transform (FFT) is connected between the MDC generator and the TC generator; and another FFT is connected between a Target Dictionary (TD) and the TC generator. ### Claim 3 (depends on 1) A system for target detection, tracking and classification directly in a compressive measurement domain in accordance to claim 1, wherein the MD is based on a Gaussian Mixture Model (GMM). ### Claim 4 (depends on 1) A system for target detection, tracking and classification directly in a compressive measurement domain in accordance to claim 1, wherein the TC is based on a Sparse Representation Classification (SRC). ### Claim 5 (depends on 4) A system for target detection, tracking and classification directly in a compressive measurement domain in accordance to claim 4, wherein the TC is using a subset of pixels in each frame. ### Claim 6 A method of target detection, tracking and classification directly in a compressive measurement domain, comprising the steps of: automatic detecting a target location in a first frame using a Gaussian Mixture Model (GMM); re-centering the target location using a Saliency-based detection; filling in missing pixels in the detected target; classifying the target using a frequency domain Sparse Representation Classification (SRC) approach; and further enhancing the target classification performance using compressed data directly. ### Claim 7 (depends on 6) A method of target detection, tracking and classification directly in a compressive measurement domain in accordance to claim 6, further comprising the steps of: saving data storage space by using the compressed data; and reducing the transmission bandwidth of videos. ### Claim 8 (depends on 6) A method of target detection, tracking and classification directly in a compressive measurement domain in accordance to claim 6, further comprising the steps of: repeating the re-centering process multiple times to improve accuracy of the target location. ### Claim 9 (depends on 6) A method of target detection, tracking and classification directly in a compressive measurement domain in accordance to claim 6, wherein a pixel completion algorithm is used to fill in the missing pixels for the captured target location. ### Claim 10 (depends on 6) A method of target detection, tracking and classification directly in a compressive measurement domain in accordance to claim 6, further comprising the steps of: transforming target templates in a dictionary and the detected targets to a frequency domain using Fast Fourier Transform (FFT). ### Claim 11 (depends on 6) A method of target detection, tracking and classification directly in a compressive measurement domain in accordance to claim 6, further comprising the steps of: applying a decision level fusion based on voting to further improve the classification performance.