Robert bosch gmbh (20240135577). METHOD FOR IDENTIFYING UNCERTAINTIES DURING THE DETECTION OF MULTIPLE OBJECTS simplified abstract
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 20240135577 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.
- Calculating feature vectors from the point cloud data using a backbone.
- Calculating anchor positions from the point cloud data through a sampling method.
- Ascertaining feature vectors from the anchor positions using an encoding.
- Calculating attention weights for cross-attention from the object queries and spatial structure.
- 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.
- Potential Applications
This technology can be applied in autonomous driving systems, surveillance systems, and robotics for object detection and tracking.
- Problems Solved
This technology helps in improving the accuracy and efficiency of object detection and tracking in complex environments.
- Benefits
The method provides a more robust and reliable way to handle uncertainties during object detection and tracking tasks.
- Potential Commercial Applications
This technology can be utilized in industries such as automotive, security, and manufacturing for enhancing object detection and tracking capabilities.
- Possible Prior Art
Prior art may include methods for object detection and tracking using machine learning algorithms and point cloud data analysis.
- Unanswered Questions
- How does this method compare to traditional object detection and tracking techniques?
This article does not provide a direct comparison to traditional methods, leaving the reader to wonder about the specific advantages and limitations of this new approach.
- What are the computational requirements for implementing this method in real-time applications?
The article does not delve into the computational resources needed for real-time implementation, leaving a gap in understanding the practical feasibility of this technology in various applications.
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.
