Object detection and tracking using machine learning transformer models with attention
US12416730B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 31, 2023 |
| Grant date | Sep 16, 2025 |
| Priority date | — |
| Expiry date | Oct 19, 2043 |
Classification
- Technology area (CPC B)Performing Operations; Transporting
- CPC primaryB60W2554/00
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
Object detection and tracking systems may use machine-learned transformer models with self-attention for detecting, classifying, and/or tracking objects in an environment. Techniques described herein may include receiving sensor data generated by different sensor modalities of a vehicle, determining different bounding shapes based on the different sensor modalities, and using a machine-learned transformer model to determine associated and/or combined bounding shapes. The machine-learned transformer model may receive a variable number of input bounding shapes representing any number of objects and various sensor modalities. Multiple stages of the transformer may be used to determine associated bounding shapes and to assign attributes for the associated bounding shapes, based on the individual bounding shapes of the different sensor modalities and/or previous bounding shapes for objects detected and tracked in a previous scene in the environment.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.