Neural Object Tracking With Offset-Aligned Radar Grids
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Solution Overview
Problem
Existing object detection and grid segmentation methods using recurrent neural networks face challenges with spatial-temporal misalignment due to object movement, leading to inaccuracies in processing radar data, especially at high speeds and finer grid resolutions.
Innovation Solution
The method involves using a trained neural network that incorporates an offset into its hidden state to account for object movement, employing techniques like dilated convolutions and dynamic deformable convolutions to align features across time steps, and utilizing attention mechanisms and sampling to improve alignment and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recurrent neural networks are used for object detection, then the system can process radar data, but spatial-temporal misalignment occurs due to object movement leading to detection inaccuracies
Solution Approach 1:
The system performs preliminary action by predicting object offsets before processing the current radar scan. The RNN predicts offset values that represent object movement between time steps, and these predicted offsets are used to pre-align the current scan with the hidden state from previous time steps, preventing spatial-temporal misalignment before detection occurs
Solution Approach 2:
The patent introduces an intermediary mechanism - the offset prediction module - that acts as a mediator between the raw radar data and the object detection process. This intermediary predicts and compensates for object movement by transforming the current scan based on predicted offsets, thereby resolving the spatial-temporal misalignment issue before the aligned data is fed into the detection head
2Measurement precision
If the grid resolution is increased to improve detection precision, then finer object details can be detected, but computational complexity increases significantly
Solution Approach 1:
The system applies segmentation by dividing the radar scan into a grid structure where each cell is processed independently through the RNN. This segmentation allows the system to handle fine-grained spatial information at high resolution while maintaining computational efficiency, as each grid cell can be processed in parallel and the RNN operates on compact feature representations rather than raw data
3Loss of information
If temporal integration of consecutive radar scans is performed, then the informative density is enhanced, but spatial-temporal misalignment due to object movement degrades the integration quality
Solution Approach 1:
The system performs preliminary action by predicting object offsets before integrating temporal information. The RNN predicts how objects have moved between time steps, and these predictions are used to pre-align the current scan with the hidden state from previous time steps. This preliminary alignment ensures that temporal integration combines information from corresponding spatial locations, maintaining alignment quality while enhancing informative density
Solution Approach 2:
The patent implements feedback by using the predicted offsets to transform and align the current radar scan before processing. The RNN's hidden state from previous time steps, which encodes information about object positions and movements, provides feedback that guides the alignment of current data. This feedback mechanism ensures that temporal integration occurs between properly aligned spatial-temporal correspondences
Data Source
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AI summary
A computer implemented method for object tracking comprises the following steps carried out by computer hardware components: providing random values as a hidden state of a trained neural network for an initial time step, wherein the hidden state represents an encoding of sensor data acquired over consecutive time steps in a grid structure, wherein the hidden state further represents an offset indicating a movement of the object between the consecutive time steps; iteratively determining an updated hidden state by processing a present hidden state and present sensor data using the trained neural network ; and determining object tracking information based on the updated hidden state.