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:
Jan. 06, 2026

Filed:

Oct. 03, 2023
Applicant:

Viettel Group, Ha Noi, VN;

Inventors:

Hong Dang Nguyen, Nam Dinh Province, VN;

Thi Hanh Vu, Hai Phong, VN;

Manh Quy Nguyen, Ha Noi, VN;

Assignee:

VIETTEL GROUP, Ha Noi, VN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/246 (2017.01); G06V 10/82 (2022.01); G06V 20/40 (2022.01);
U.S. Cl.
CPC ...
G06T 7/248 (2017.01); G06V 10/82 (2022.01); G06V 20/46 (2022.01); G06T 2207/10016 (2013.01);
Abstract

A method for multi-object tracking from video. The method includes the following steps: (1) Capturing frames from the streaming source and preprocess the data; (2) Extract video features with three choices: a 3D-CNN backbone followed by a Transformer Encoder, a Video Transformer Encoder, a 2D-CNN Encoder with a stack of frames as input followed by a Transformer Encoder; (3) Multi-object tracking using a new end-to-end multi-task deep learning model named JDAT (Joint Detection Association Transformer), then post-processing and updating tracking state with Temporal Aggregation Module (TAM). The deep learning models in step 2 and step 3 are trained simultaneously end-to-end with a loss function that is accumulated over multiple timesteps (Collective Average Loss—CAL). Also, the model can be pretrained with weakly labeled image dataset in a self-supervised learning manner first, then finetuned on supervised video datasets with full tracking labels.


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