# US 12,762,860 B2 · Driving video recording system and a controlling method of the same and a manufacturing method of the same > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,762,860 B2 - Document ID: US12762860B2 - Status: Granted - Kind: B2 - Application number: 18/522,004 - Filed: 2023-11-28 - Published: 2025-02-20 - Granted: 2026-10-13 - Earliest claimed: 2023-08-14 - Assignees: Hyundai Motor Company; Kia Corporation; Sogang University Research & Business Development Foundation - Applicants: Hyundai Motor Company; Kia Corporation; Sogang University Research & Business Development Foundation - Inventors: Dong Hyuk Jeong; Gyun Ha Kim; Seok Ju Yeom; Jae Ho Kwak; Jung Hoon Lee; Seung Hun Moon; Suk Ju Kang; Chang Ryeol Jeon - Web page: https://patentgrep.com/patent/US12762860B2 ## CPC - G06V 20/41: {Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items (segmenting video sequences G06V20/49)} - G06V 10/774: Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting - G06V 10/82: using neural networks - G06V 20/46: {Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames} - G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract In a driving video recording system and a manufacturing method of the same, the driving video recording system includes a camera module for monitoring the surroundings of a vehicle; then first memory for storing a video transmitted from the camera module; the second memory for storing a computer program for controlling the storage of the video; and a controller including a processor for executing the computer program, wherein the computer program includes a contamination classification deep training network model, and the processor is configured to determine whether video data obtained by the camera module through the deep-learning network model is contaminated through the execution of the computer program. ## Claims ### Claim 1 A driving video recording system comprising: a camera module for monitoring surroundings of a vehicle; a first memory for storing a video transmitted from the camera module; a second memory for storing a computer program for controlling storage of the video; and a controller including a processor electrically and communicatively connected to the camera, the first memory and the second memory and configured to execute the computer program, wherein the computer program includes a contamination classification deep-learning network model, and the processor is further configured to determine whether video data obtained by the camera module includes contamination data through the deep-learning network model by executing the computer program, wherein the deep-learning network model has been trained by distribution-based separation training with non-contamination training data after a classification, wherein the distribution-based separation training includes: extracting a plurality of first feature values for contamination training data through the deep-learning network model; extracting a plurality of second feature values for the non-contamination training data; and determining a threshold value based on plurality of first feature value distributions and plurality of second feature value distributions. ### Claim 2 (depends on 1) The driving video recording system of claim 1, wherein the processor is further configured to extract a feature value from the video data through the deep-learning network model and determine whether the video data includes the contamination data by comparing the feature value with a set threshold value. ### Claim 3 (depends on 2) The driving video recording system of claim 2, wherein the processor is further configured to extract a feature for image data of a single frame of the video data for the feature value. ### Claim 4 (depends on 1) The driving video recording system of claim 1, wherein the processor is further configured to determine the classification for the contamination data among predetermined contamination type classifications through the deep-learning network model when the processor concludes that the video data includes the contamination data. ### Claim 5 (depends on 4) The driving video recording system of claim 4, wherein the contamination type classifications includes at least one of a dust, a soil, an ice, or a water droplet. ### Claim 6 (depends on 4) The driving video recording system of claim 4, wherein the deep-learning network model has been trained by classification training with training data for each contamination type. ### Claim 7 A control method of a driving video recording system including a camera module for monitoring surroundings of a vehicle, a first memory for storing a video transmitted from the camera module, a second memory for storing a computer program for controlling storage of the video, and a controller including a processor electrically and communicatively connected to the camera, the first memory and the second memory and configured for executing the computer program, wherein the computer program includes a contamination classification deep-learning network model, the control method comprising: receiving, by the processor, video data from the camera module; and determining, by the processor, whether the video data includes contamination data through the deep-learning network model by executing the computer program, wherein the deep-learning network model has been trained by distribution-based separation training with non-contamination training data after classification, wherein the distribution-based separation training includes: extracting a plurality of first feature values for the contamination training data through the deep-learning network model; extracting a plurality of second feature values for the non-contamination training data; and determining a threshold value based on plurality of first feature value distributions and plurality of second feature value distributions. ### Claim 8 (depends on 7) The control method of claim 7, wherein the determining of whether the video data includes the contamination data includes: extracting a feature value from the video data through the deep training network model; and comparing the feature value with a set threshold value to determine whether the video data includes the contamination data. ### Claim 9 (depends on 8) The control method of claim 8, wherein the extracting of the feature value includes extracting a feature for image data of a single frame of the video data. ### Claim 10 (depends on 7) The control method of claim 7, further including determining the classification for the contamination data among predetermined contamination type classification through the deep-learning network model when the processor concludes that the video data includes the contamination data. ### Claim 11 (depends on 10) The control method of claim 10, wherein the contamination type classifications includes at least one of a dust, a soil, an ice, or a water droplet. ### Claim 12 (depends on 10) The control method of claim 10, wherein the deep-learning network model has been trained by classification training with training data for each contamination type. ### Claim 13 A method for manufacturing a driving video recording system including a camera module for monitoring surroundings of a vehicle a first memory for storing a video transmitted from the camera module, a second memory for storing a computer program for controlling storage of the video and including a contamination classification deep-learning network model, and a controller including a processor electrically and communicatively connected to the camera, the first memory and the second memory and configured for executing the computer program, the method comprising: training the deep-learning network model by classification training with training data for each contamination type, wherein the method further includes training the deep-learning network model by distribution-based separation training with non-contamination training data after the classification training, wherein the distribution-based separation training includes: extracting a plurality of first feature values for the contamination training data through the deep-learning network model; extracting a plurality of second feature values for the non-contamination training data; and determining a threshold value based on plurality of first feature value distributions and plurality of second feature value distributions. ### Claim 14 (depends on 13) The manufacturing method of claim 13, wherein the classification for each contamination type includes at least one of a dust, a soil, an ice, or a water droplet.