Collision Prediction via Local Scale Change and Translational Motion
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Solution Overview
Problem
Current collision prediction systems for moving objects, such as vehicles and pedestrians, face challenges in accurately determining collision likelihood and time to collision, especially with semi-rigid obstacles and non-planar motions, often requiring complex calibration and object recognition processes.
Innovation Solution
A method utilizing computer vision to analyze digital video frames for local scale change and translational motion of features, calculating the likelihood of collision by comparing these metrics to determine the collision point and time to collision, without the need for explicit obstacle recognition or camera calibration, using a system that includes a video camera and motion sensors to provide collision warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex calibration and object recognition processes are used to determine collision likelihood, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential motion parameters (local scale change and translational motion) needed for collision prediction, eliminating the need for complex object recognition and calibration processes. By focusing solely on motion characteristics rather than full object identification, the system achieves accurate collision likelihood determination without the computational burden of complex vision algorithms.
Solution Approach 2:
The system uses the motion information inherently present in the video frames to determine collision parameters. The local scale change and translational motion are directly derived from the visual data without requiring external calibration data or complex object models. The method is self-contained, using only the video stream and basic motion analysis to predict collisions.
2Measurement precision
If explicit obstacle recognition is performed, then object identification accuracy is improved, but processing time increases
Solution Approach 1:
The patent extracts only the motion-related features (local scale change and translational motion) from video frames, completely bypassing the need for full obstacle recognition and classification. This extraction approach maintains sufficient accuracy for collision prediction while dramatically reducing processing time, as the system only tracks motion parameters rather than identifying and categorizing objects.
Solution Approach 2:
The system performs only the partial action necessary for collision prediction—analyzing motion parameters—rather than the complete action of full obstacle recognition. By doing less (focusing only on motion rather than full object identification), the system achieves real-time performance suitable for safety-collision applications where speed is critical.
3Measurement precision
If camera calibration is performed, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system is self-calibrating in the sense that it does not require external calibration procedures. The local scale change and translational motion are computed directly from the video frames using intrinsic geometric relationships, eliminating the need for calibration targets, known distances, or manual parameter input. The method automatically adapts to different camera configurations without user intervention.
Solution Approach 2:
The patent removes the calibration step entirely from the system workflow. By formulating collision prediction in terms of local scale change and translational motion that can be directly measured from video frames, the system eliminates the need for camera intrinsic parameter calibration, making the system immediately operational upon video input without any setup procedures.
4Reliability
If complex object recognition processes are used, then reliability of collision prediction is improved, but device complexity increases
Solution Approach 1:
The patent extracts the critical collision-related motion information (local scale change and translational motion) while discarding all other object characteristics. This selective extraction maintains prediction reliability by focusing on the parameters that directly indicate collision risk, while eliminating the complexity of full object recognition systems including classification, identification, and tracking of multiple object types.
Solution Approach 2:
Instead of using complex object recognition to infer motion and collision risk, the patent inverts the approach by directly measuring motion parameters from video frames and using them to predict collisions. This inversion simplifies the system by working directly with motion data rather than deriving motion from complex object models and recognition results.
Data Source
AI summary
In some implementations, there is provided a method. The method may include receiving data characterizing a plurality of digital video frames; detecting a plurality of features in each of the plurality of digital video frames; determining, from the detected features, a local scale change and a translational motion of one or more groups of features between at least a pair of the plurality of digital video frames; and calculating a likelihood of collision. Related apparatus, systems, techniques, and articles are also described.