Adaptive Weighted Grid for Region of Interest Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing camera systems for driver assistance lack an efficient method to dynamically adjust the region of interest in image frames, especially in scenarios with minimal image contrast and ego-motion, which affects obstruction detection and road environment analysis.
Innovation Solution
A camera system with a pre-processing circuit that adapts weights on a low-resolution grid to specify a region of interest, allowing for flexible adjustment of the region's position and shape, using multiplicative weighting factors and image parameters like intensity and gradient, to maintain the region of interest despite camera or object motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a low-resolution grid with adaptive weights is used to define region of interest, then the flexibility to adjust region position and shape is improved, but the processing complexity increases
Solution Approach 1:
The image is divided into a low-resolution grid of cells, where each cell represents a group of picture elements. This segmentation allows the region of interest to be defined by selecting and weighting specific cells, providing flexibility while keeping the number of parameters manageable through coarse-grained division rather than fine-grained pixel-level control.
Solution Approach 2:
Different weights are assigned to different cells in the grid, with non-zero weights for cells within the region of interest and zero weights for cells outside it. This local differentiation allows the system to focus processing resources on relevant regions while adapting the region's position and shape by modifying individual cell weights.
2Measurement precision
If weights are adaptively changed responsive to scene changes, then the detection accuracy in varying conditions is improved, but the computational load increases
Solution Approach 1:
The weights assigned to grid cells are made dynamic and adaptive, changing in response to detected scene changes such as ego-motion or image motion of tracked objects. This allows the region of interest to automatically track and adapt to moving targets or changing camera orientations, maintaining detection accuracy without requiring continuous full-image processing.
Solution Approach 2:
The system uses the detected motion information from the scene itself to automatically adjust the weight distribution and region of interest positioning. The motion detection results directly drive the weight adaptation, creating a self-regulating system that maintains accuracy while minimizing unnecessary computational operations.
3Stability of the object's composition
If the region of interest is maintained despite camera or object motion, then the consistency of analysis is improved, but the complexity of tracking and adjustment increases
Solution Approach 1:
The system implements a feedback mechanism where motion detection results are used to update the weight distribution and region of interest positioning. The detected ego-motion or object motion feeds back into the weight adjustment process, creating a closed-loop system that automatically compensates for motion and maintains region consistency without manual intervention.
Solution Approach 2:
The system performs preliminary motion detection and region adjustment before the actual image analysis. By detecting motion in advance and pre-adjusting the weight distribution and region of interest position, the system ensures that the analysis is always performed on the correct region, maintaining consistency while simplifying the overall processing pipeline.
Data Source
AI summary
An image sensor and a pre-processing circuit adapted to associate weights respectively with cells of a grid. The cells respectively may include multiple contiguous picture elements of the image sensor. The pre-processing circuit may be configured to adaptively change the weights responsive to changes in the scene being imaged by the image sensor. The number of the cells per distance on the surface of the image sensor may be substantially less than the resolution of the image sensor. The cells which share the same weights may specify a region of interest on the image sensor.