{
  "$type": "site.standard.document",
  "description": "An object recognition method using queue-based model selection and optical flow in an autonomous driving environment includes preprocessing data through a dense flow in a matrix form by calculating an optical flow of images captured consecutively in time by a sensor for an autonomous vehicle…",
  "path": "/patents/1310131",
  "publishedAt": "2022-02-03T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "G06K9/00805",
    "Korea University Research and Business Foundation"
  ],
  "textContent": "An object recognition method using queue-based model selection and optical flow in an autonomous driving environment includes preprocessing data through a dense flow in a matrix form by calculating an optical flow of images captured consecutively in time by a sensor for an autonomous vehicle, generating a confidence mask by generating a vectorized confidence threshold representing a probability that there is a moving object for each cell of the preprocessed matrix, determining whether there is a moving object on the images by mapping the images captured consecutively in time to the confidence mask, and selecting an object recognition model using a tradeoff constant between object recognition accuracy and queue stability in each time unit. Accordingly, it is possible to improve the performance of object recognition in an autonomous driving environment by applying the optical flow to the confidence threshold of the object recognition system.",
  "title": "METHOD FOR OBJECT RECOGNITION USING QUEUE-BASED MODEL SELECTION AND OPTICAL FLOW IN AUTONOMOUS DRIVING ENVIRONMENT, RECORDING MEDIUM AND DEVICE FOR PERFORMING THE METHOD"
}