Vehicle Lamp Tracking via Multiple Scale Measurement Model
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Solution Overview
Problem
Existing vehicle tracking technologies fail to reliably track complex motion lights due to changes in vehicle speed, resulting in poor tracking reliability.
Innovation Solution
A system and method utilizing an image capture device and processor to analyze vehicle lamp dynamic motion information and multiple scale variation information, applying a multiple scale vehicle lamp measurement model to predict and update the position of vehicle lamps, while integrating adaptive sampling and kernel functions to filter noise and improve tracking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing vehicle tracking technologies are used, then the system is simple, but the tracking reliability is poor due to inability to handle complex motion lights and vehicle speed changes
Solution Approach 1:
The patent segments the tracking problem into multiple components: detection stage (initial lamp detection), prediction stage (motion prediction using dynamic motion information), and evolution stage (sample evolution with multiple scale variations). This segmentation allows each stage to be optimized independently, improving overall tracking reliability while managing system complexity through modular design
Solution Approach 2:
The patent implements dynamics by using adaptive sampling ranges that adjust according to vehicle speed and lamp motion characteristics. The system dynamically updates the prediction model based on real-time dynamic motion information (center position, moving speed, moving angle), enabling the tracker to adapt to changing vehicle speeds and complex lamp motions, thereby improving tracking reliability
2Measurement precision
If multiple scale vehicle lamp measurement model is applied, then the measurement precision is improved, but the calculation complexity increases
Solution Approach 1:
The patent applies partial action by selectively using multiple scale variations only when necessary for accurate tracking. The system calculates adaptive sampling ranges based on variance matrices and only performs evolution sampling within these optimized ranges, rather than exhaustively searching all possible scales. This reduces calculation complexity while maintaining measurement precision for lamp position tracking
Solution Approach 2:
The patent changes parameters by using multiple scale variations (different spatial resolutions) to represent the vehicle lamp at different levels of detail. The system dynamically adjusts which scales to use based on the detection results and prediction outcomes, optimizing the balance between measurement precision and calculation complexity through parameter adaptation
3Measurement precision
If adaptive sampling range is calculated based on variance matrix, then the tracking accuracy is improved, but the processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating the variance matrix from previous samples and using it to determine the adaptive sampling range before performing the actual evolution sampling. This preparation work is done in advance, allowing the main tracking algorithm to proceed more efficiently with predefined sampling boundaries, thus reducing overall processing time while maintaining tracking accuracy
Solution Approach 2:
The patent uses copying by generating multiple evolution samples within the adaptive sampling range and selecting the best match. Instead of performing complex calculations for each possible lamp position, the system creates candidate samples (copies) based on the variance matrix and selects the optimal one, reducing processing time while preserving tracking accuracy
Data Source
AI summary
A system of detection, tracking and identification of an evolutionary adaptation of a vehicle lamp includes an image capture device and a processor. The image capture device captures an image of a vehicle. The processor processes the image of the vehicle to generate a detection result of the vehicle lamp, analyzes and integrates vehicle lamp dynamic motion information and vehicle lamp multiple scale variation information based on the detection result, and then tracks the position of the vehicle lamp by applying a multiple scale vehicle lamp measurement model.