The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 08, 2022

Filed:

Jun. 08, 2022
Applicant:

Huazhong University of Science and Technology, Hubei, CN;

Inventors:

You Yang, Hubei, CN;

Qian Kan, Hubei, CN;

Xinshengzi Huang, Hubei, CN;

Qiong Liu, Hubei, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/246 (2017.01); G06Q 10/04 (2012.01); G06V 40/20 (2022.01); G06V 20/40 (2022.01); G06V 10/44 (2022.01); G06V 10/42 (2022.01);
U.S. Cl.
CPC ...
G06V 40/20 (2022.01); G06V 10/42 (2022.01); G06V 10/457 (2022.01); G06V 20/46 (2022.01);
Abstract

The present invention discloses a pedestrian trajectory prediction method and system based on a multi-interaction spatio-temporal graph network, which belong to the field of pedestrian trajectory prediction. The method includes: extracting multi-interaction features of pedestrians in video frames; for each frame in a video sequence, abstracting each pedestrian in the frame as a vertex, connecting the pedestrian(s) with other pedestrians to form an edge(s), where the vertex attribute is the multi-interaction feature of the pedestrian so as to obtain a multi-interaction spatio-temporal graph network; for each multi-interaction spatio-temporal graph, obtaining spatial dependencies of each pedestrian with other pedestrians in the spatio-temporal graph, and optimizing the attribute of each vertex through the spatial dependencies between pedestrians; the vertices of adjacent time points of the pedestrians are connected to obtain time dependencies of the pedestrians, and then infer a trajectory of a future time point(s).


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