Vehicle Eccentricity Mapping for Real-Time Obstacle Detection
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Solution Overview
Problem
Existing vehicle navigation systems struggle to accurately and efficiently determine the speed and direction of moving objects in real-time, especially in autonomous and semi-autonomous vehicle operations, due to limitations in processing video stream data without prior information or complex user-defined parameters, and fail to handle concept drift or evolution in pixel data over time.
Innovation Solution
The method involves creating an eccentricity map based on per-pixel mean and variance from video stream data, detecting moving objects by thresholding the map, and transforming their motion components into global coordinates for vehicle path determination, allowing the vehicle to avoid obstacles without requiring user-defined parameters or offline training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional video stream processing methods are used to determine moving object speed and direction, then the system can operate without specialized preprocessing, but the processing speed is insufficient to achieve real-time performance at over 100 frames per second
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing eccentricity values for each pixel in the video stream. The eccentricity map is computed in advance using historical pixel data, allowing the system to quickly identify moving objects through simple thresholding operations rather than complex real-time analysis, thereby achieving over 100 frames per second processing speed while maintaining accurate speed and direction estimation.
2Measurement precision
If complex user-defined parameters or offline training are used to improve detection accuracy, then measurement precision improves, but the system complexity and computational burden increase significantly
Solution Approach 1:
The system applies self-service by automatically adapting to concept drift and evolution in pixel data without requiring user-defined parameters or offline training. The eccentricity map is dynamically updated using a forgetting factor that automatically adjusts to changing conditions, allowing the system to maintain high detection accuracy while keeping the implementation simple and free of complex configuration requirements.
3Adaptability or versatility
If traditional background subtraction methods are used, then the system can detect moving objects, but it fails to handle concept drift or evolution in pixel data over time
Solution Approach 1:
The patent applies dynamics by implementing a time-varying eccentricity map that continuously adapts to changing conditions in the video stream. The system uses a forgetting factor to dynamically update the eccentricity values, allowing it to track concept drift and evolution in pixel data while maintaining reliable detection of moving objects. This dynamic adaptation ensures the system remains robust against changes in lighting, camera motion, and scene variations.
Data Source
AI summary
A computer, including a processor and a memory, the memory including instructions to be executed by the processor to detect a moving object in video stream data based on determining an eccentricity map. The instructions can further include instructions to determine a magnitude and direction of motion of the moving object, transform the magnitude and direction to global coordinates and operate a vehicle based on the transformed magnitude and direction.