公开(公告)号：US20210350146A1

公开(公告)日：2021-11-11

申请号：US17379448

申请日：2021-07-19

Applicant: Beijing Baidu Netcom Science and Technology Co., Ltd.

Inventor： Wei ZHANG ,  Xiao TAN ,  Hao SUN ,  Errui DING

IPC: G06K9/00 ,  G06K9/62 ,  G06T7/215 ,  G06T7/246

Abstract: A vehicle tracking method, apparatus, and electronic device relate to the technical field of computer vision and deep learning. A method includes identifying first position information of a first vehicle in a first image of a video stream collected during driving of vehicles; and identifying second position information of a second vehicle in a second image of the video stream. The first image is the previous N frame images adjacent to the second image in the video stream, and N is a positive integer. The method also includes predicting first position offset information of the second vehicle relative to the first vehicle on the basis of the first image and the second image; and determining a tracking result of the second vehicle on the basis of the first position information, the second position information and the first position offset information.

公开(公告)号：US20190384810A1

公开(公告)日：2019-12-19

申请号：US16176783

申请日：2018-10-31

Applicant: BEIJING BAIDU NETCOM SCIENCE AND TECHNOLOGY CO., LTD.

Inventor： Jizhou HUANG ,  Yaming SUN ,  Wei ZHANG ,  Haifeng WANG

IPC: G06F17/27 ,  G06N7/00 ,  G06K9/00 ,  G06K9/62 ,  G06F17/30

Abstract: The present disclosure provides a method of training a descriptive text generating model, and a method and apparatus for generating a descriptive text, wherein the method of training a descriptive text generating model comprises: obtaining training data, the training data comprising: a notional word, a first descriptive text and a second descriptive text of the notional word, wherein the second descriptive text is a concise expression of the first descriptive text; regarding the notional word and the first descriptive text of the notional word as input of a seq2seq model, regarding the second descriptive text of the notional word as output of the seq2sequ model, and training the seq2seq model to obtain a descriptive text generating model. The descriptive text generating model according to the present disclosure can implement generation of a concise descriptive text with respect to the notional word in a deep understanding manner.