18490369. METHOD FOR IDENTIFYING UNCERTAINTIES DURING THE DETECTION OF MULTIPLE OBJECTS simplified abstract (Robert Bosch GmbH)
Contents
- 1 METHOD FOR IDENTIFYING UNCERTAINTIES DURING THE DETECTION OF MULTIPLE OBJECTS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD FOR IDENTIFYING UNCERTAINTIES DURING THE DETECTION OF MULTIPLE OBJECTS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHOD FOR IDENTIFYING UNCERTAINTIES DURING THE DETECTION OF MULTIPLE OBJECTS
Organization Name
Inventor(s)
Felicia Ruppel of Renningen (DE)
METHOD FOR IDENTIFYING UNCERTAINTIES DURING THE DETECTION OF MULTIPLE OBJECTS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18490369 titled 'METHOD FOR IDENTIFYING UNCERTAINTIES DURING THE DETECTION OF MULTIPLE OBJECTS
Simplified Explanation
The patent application describes a method for identifying uncertainties during the detection and/or tracking of multiple objects from point cloud data using a transformer with an attention model. The method involves calculating feature vectors from the point cloud data, determining anchor positions, ascertaining feature vectors from the anchor positions, calculating attention weights for cross-attention, and determining the greatest attention weights for each object query.
- Calculating feature vectors from point cloud data using a backbone as key vectors for the transformer.
- Determining anchor positions from the point cloud data through a sampling method.
- Ascertaining feature vectors from the anchor positions as object queries for the transformer.
- Calculating attention weights for cross-attention from the object queries and spatial structure.
- Determining the greatest attention weights for each object query.
- Calculating a covariance matrix for the greatest attention weights.
- Calculating the determinant of the covariance matrix to obtain an attention spread.
Potential Applications
This technology could be applied in autonomous vehicles for object detection and tracking, robotics for navigation and object manipulation, and surveillance systems for monitoring and tracking multiple objects.
Problems Solved
This technology addresses the challenge of identifying uncertainties in object detection and tracking, improves the accuracy of tracking multiple objects, and enhances the efficiency of processing point cloud data.
Benefits
The benefits of this technology include improved object detection and tracking accuracy, enhanced performance in complex environments, and increased reliability in real-time applications.
Potential Commercial Applications
Commercial applications of this technology could include autonomous driving systems, warehouse automation for inventory management, and security systems for monitoring and tracking objects in large areas.
Possible Prior Art
One possible prior art for this technology could be related to object detection and tracking algorithms using machine learning models and point cloud data processing.
What are the limitations of this technology in real-world applications?
One limitation of this technology in real-world applications could be the computational resources required for processing large amounts of point cloud data in real-time.
How does this technology compare to existing object detection and tracking methods?
This technology offers a unique approach by using a transformer with an attention model to identify uncertainties during object detection and tracking, which may provide more accurate and reliable results compared to traditional methods.
Original Abstract Submitted
A method for identifying uncertainties during the detection and/or tracking of multiple objects from point cloud data using a transformer with an attention model. The state of the tracked objects is stored in the feature space. The method includes: calculating feature vectors from the point cloud data by means of a backbone, wherein the feature vectors serve as key vectors for the transformer; calculating anchor positions from the point cloud data by means of a sampling method; ascertaining feature vectors from the anchor positions using an encoding, wherein the feature vectors serve as object queries for the transformer; calculating attention weights for cross-attention from the object queries and a spatial structure used by the backbone; determining the greatest attention weights of the transformer for each object query; calculating a covariance matrix for the greatest attention weights; calculating the determinant of the covariance matrix to obtain an attention spread.
