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公开(公告)号：US20220261577A1
公开(公告)日：2022-08-18
申请号：US17518411
申请日：2021-11-03
Inventor： Seung Woo NAM , Jang Woon BAEK , Joon-Goo LEE , Kil Taek LIM , Byung Gil HAN
Abstract: A re-identification apparatus acquires a first image in which a tracking target entering an intersection is captured, and identifies the tracking target and targets having a predetermined positional relationship with the tracking target in the first image. The re-identification apparatus selects a camera to be used for re-identification of the tracking target based on a signal system of the intersection, and acquires a second image captured by the selected camera and one or more third images before or after the second image. The re-identification apparatus determines a target identified in the second image and the third images among the targets when identifying an object corresponding to the tracking target in the second image, and determines whether the re-identification of the tracking target is successful based on the targets identified in the first image and the target identified in the second image and the third images.
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公开(公告)号：US20220148193A1
公开(公告)日：2022-05-12
申请号：US17522469
申请日：2021-11-09
Inventor： Yun Won CHOI , Jang Woon BAEK , Joon Goo LEE , Kil Taek LIM
Abstract: The present invention is directed to solving the existing problems and provides an apparatus and method for optimizing object detection performance by re-learning data specific to an installed location from an online server using a localization module in an edge terminal receiving a fixed image like a closed circuit television (CCTV) camera.
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公开(公告)号：US20220147773A1
公开(公告)日：2022-05-12
申请号：US17497771
申请日：2021-10-08
Inventor： Joon Goo LEE , Jang Woon BAEK , Kil Taek LIM , Yun Won CHOI , Byung Gil HAN
Abstract: Provided is a method of creating a local database for local optimization of an object detector based on a deep neural network. The method includes performing preprocessing on an image extracted from real-time collected or pre-collected images from an edge device, modeling a static background image based on the image received through the pre-processing unit and calculating a difference image between a current input image and a background model to model a dynamic foreground image, detecting an object image from the image based on a training model, and creating a local database based on the background image, the foreground image synthesized with the background image, and the object image synthesized with the background image.
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