Transformer 3D Box Tracking for Cross-Frame Object Linking
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Solution Overview
Problem
Existing methods for tracking 3D bounding boxes in real-time driving environments have not been effectively addressed in existing technologies, leading to inefficient and inefficient tracking of 3D bounding boxes in real-time driving environments, which can lead to inaccurate tracking of objects in autonomous vehicles.
Innovation Solution
The use of a transformer-based bounding box tracking system that utilizes object queries to generate 3D bounding boxes and enriches bounding box embeddings through transformer layers with self-attention and feed forward stages, enabling accurate linking and identification of false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional tracking methods are used for 3D bounding boxes, then the system complexity remains low, but the tracking accuracy and reliability deteriorate in real-time driving environments
Solution Approach 1:
The patent replaces traditional mechanical tracking algorithms with a transformer-based neural network system. The transformer architecture uses self-attention mechanisms and feed-forward stages to process bounding box embeddings, substituting conventional tracking methods with a deep learning approach that achieves superior tracking accuracy and false positive reduction in real-time driving environments.
2Measurement precision
If transformer layers with self-attention are used to enrich bounding box embeddings, then the linking accuracy across time steps improves, but the computational complexity increases
Solution Approach 1:
The transformer-based tracking system is divided into distinct functional modules: self-attention stages for capturing temporal dependencies, feed-forward stages for feature transformation, and embedding enrichment components. This segmentation allows each module to specialize in specific computational tasks, improving linking accuracy while enabling optimized computation and resource allocation.
3Reliability
If existing tracking methods are used, then the processing speed remains fast, but the false positive rate increases and object identification accuracy decreases
Solution Approach 1:
The system performs preliminary action by enriching bounding box embeddings with transformer layers before the actual tracking and identification processes. This pre-processing step enhances the quality of bounding box representations, enabling more accurate object identification and false positive reduction in subsequent tracking operations, thereby improving overall reliability without significantly impacting processing efficiency.
Data Source
AI summary
A perception system may be used to generate bounding boxes for objects in a vehicle scene. The perception system may receive images and feature maps corresponding to the received images. The perception system may link bounding boxes to bounding boxes from a previous time steps and identify false positive bounding boxes. The system can link 3D boxes of the same object from the different frames, by taking the 3D boxes in a time step as input. The system can sue transformer self-attention to exchange information between 3D boxes to learn global-informative box embeddings. Similarity between these learned embeddings can be used to link the boxes of the same object.