Feature-Based Forward Collision Detection Beyond Bounding Box Noise
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Solution Overview
Problem
Conventional object detection systems for collision estimation in vehicles suffer from inaccuracies due to 'breathing bounding boxes', leading to false warnings or missed collision alerts in dynamic driving environments.
Innovation Solution
A neural network regression model is used to estimate a bounding box around detected objects, aligning features across frames with a transformation matrix to accurately track objects, reducing noise and improving collision detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional object detection techniques are used to estimate object size for collision detection, then the system can operate in real-time, but the measurement precision of object size changes deteriorates due to bounding box breathing noise
Solution Approach 1:
The patent segments the object detection process into two distinct stages: (1) a coarse detection stage using a neural network to identify objects and generate initial bounding boxes, and (2) a fine tracking stage using feature point detection and transformation matrices to precisely measure size changes. This segmentation allows the system to maintain real-time performance while achieving sub-pixel measurement precision by not requiring the neural network to perfectly delineate object boundaries in every frame.
Solution Approach 2:
The patent introduces feature points (corners, edges, or other distinctive markers) as intermediary elements between the neural network detection and the size measurement process. These feature points serve as stable reference markers that can be tracked across frames with high precision, decoupling the size measurement accuracy from the bounding box estimation accuracy. The transformation matrix computed from feature point correspondences becomes the intermediary mechanism that translates visual observations into precise size change measurements.
2Measurement precision
If feature-based tracking with transformation matrices is used to improve measurement precision, then the measurement precision of object size changes improves, but the device complexity increases
Solution Approach 1:
The patent performs preliminary action by having the neural network generate bounding boxes and identify regions of interest before the feature-based tracking begins. This preliminary detection step provides constraints and guidance for the subsequent feature point selection and matching process, reducing the search space and computational burden. By preparing the detection results in advance, the system avoids the need for exhaustive feature matching across the entire image, thereby managing complexity while maintaining precision.
3Device complexity
If simple object recognition is used for collision detection, then the device complexity is reduced, but the reliability of collision detection deteriorates due to false warnings and missed alerts
Solution Approach 1:
The patent replaces the mechanical approach of relying solely on neural network bounding box measurements with an optical-computational approach using feature point correspondence and transformation matrices. This substitution leverages the stability of geometric features and the mathematical rigor of transformation geometry to achieve more reliable size change measurements, thereby improving collision detection reliability without requiring a complete redesign of the system architecture.
Data Source
AI summary
Disclosed herein are systems and methods for tracking objects to detect a potential collision with a vehicle. A computing device can receive a first frame and a second frame captured by a capture device mounted to the vehicle. The computing device can then identify first points of interest corresponding to an object identified in the first frame and second points of interest corresponding to the object identified in the second frame. The computing device can then generate a transformation matrix that specifies a transformation of the first points of interest to the second points of interest. The computing device can then determine, based on the transformation matrix, a score corresponding to a potential collision event, and transmit a collision signal to a collision alert device responsive to the score satisfying a potential collision event threshold.