3D Bounding Box Selection for Accurate Object Tracking
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Solution Overview
Problem
Autonomous systems, such as vehicles and drones, face challenges in accurately representing and tracking three-dimensional objects in environments due to the generation of multiple bounding boxes with variations in size, shape, orientation, and confidence levels, which complicates object tracking and navigation.
Innovation Solution
A machine learning model is trained to select or generate a bounding box that best represents an object based on characteristics like velocity, distance, geolocation, and confidence levels, using annotated sensor data and ground truth bounding boxes to improve the accuracy of object representation and tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple three-dimensional bounding boxes are generated for an object using various techniques, then the coverage and detection capability are improved, but the complexity of selecting a single representative bounding box increases
Solution Approach 1:
The patent changes the parameters of the bounding boxes by associating each bounding box with specific characteristics (velocity, distance, geolocation, confidence level). The machine learning model uses these parameter variations to select the most appropriate bounding box for the current situation, resolving the contradiction by making the selection process parameter-driven rather than manually complex.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the multiple bounding box generation techniques and the single bounding box selection requirement. This intermediary automatically processes the multiple bounding boxes and their associated characteristics to select the most representative one, eliminating the need for manual selection complexity while maintaining high detection accuracy.
2Ease of operation
If a single three-dimensional bounding box is selected for object tracking, then the tracking process is simplified, but the accuracy of object representation may be reduced
Solution Approach 1:
The machine learning model serves as an intermediary that automatically selects the most accurate bounding box representation, simplifying the tracking process while maintaining high precision. The model considers multiple bounding boxes and their characteristics to make the selection, providing both simplicity for tracking and accuracy for representation.
Solution Approach 2:
The system uses parameter changes by evaluating bounding boxes based on their associated characteristics (confidence level, velocity, distance, geolocation). The machine learning model selects the bounding box with the most appropriate parameters for the current situation, ensuring both tracking simplicity and representation accuracy.
3Adaptability or versatility
If multiple bounding boxes with different characteristics are processed, then the adaptability to various situations is improved, but the processing resources required increase
Solution Approach 1:
The machine learning model acts as an efficient intermediary that processes multiple bounding boxes and their characteristics to select the most appropriate one. While it does consume processing resources, the model is trained offline and performs rapid inference during runtime, providing adaptability with optimized resource usage compared to processing all bounding boxes equally.
Solution Approach 2:
The system uses parameter changes by evaluating bounding boxes based on situation-specific characteristics (velocity, distance, geolocation, confidence level). The machine learning model learns to weigh these parameters differently based on the situation, providing adaptability while optimizing processing resources by focusing on the most relevant parameters for each context.
Data Source
AI summary
Techniques to train a model with machine learning and use the trained model to select a bounding box that represents an object are described. For example, a system may implement various techniques to generate multiple bounding boxes for an object in an environment. Each bounding box may be slightly different based on the technique and data used. To select a bounding box that most closely represents an object (or is best used for tracking the object), a model may be trained. The model may be trained by processing sensor data that has been annotated with bounding boxes that represent ground truth bounding boxes. The model may be implemented to select a most appropriate bounding box for a situation (e.g., a given velocity, acceleration, distance, location, etc.). The selected bounding box may be used to track an object, generate a trajectory, or otherwise control a vehicle.