Context-Based Vehicle-Background Separation in Video Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge of accurately separating background pixels from moving vehicle pixels in computer vision applications is inherently ill-defined, leading to ambiguity in pixel tracking between frames.
Innovation Solution
A context-based method for separating on-vehicle and off-vehicle points of interest in videos, involving the use of bounding boxes and normalized relative locations to distinguish between vehicle-related and background pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pixel tracking methods are used to track pixels between frames, then the process is simple and fast, but the separation of vehicle pixels from background pixels is inaccurate and ill-defined
Solution Approach 1:
The patent segments the image into multiple regions of interest (ROIs) including vehicle ROIs and background ROIs. By dividing the image space into distinct segments and tracking pixels within each segment separately, the method achieves more accurate vehicle-background separation while maintaining computational efficiency through localized processing.
Solution Approach 2:
The patent introduces background ROIs as intermediary elements between the vehicle and the rest of the background. These intermediary regions provide context information that helps disambiguate edge pixels, allowing the system to determine whether pixels belong to the vehicle or background with higher accuracy.
2Measurement precision
If context-based separation with multiple ROIs is implemented, then pixel tracking accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
By segmenting the image into focused ROIs rather than processing the entire image, the method reduces the computational domain. Each ROI is processed independently with dedicated tracking, which improves accuracy for critical regions while reducing overall processing time compared to full-image analysis.
Solution Approach 2:
The patent applies partial action by focusing computational resources only on specific ROIs that contain vehicles or relevant background context, rather than processing every pixel in the entire image. This selective approach achieves high accuracy where needed while minimizing unnecessary computations in irrelevant regions.
Data Source
AI summary
A method for context based separation between vehicle pixels and background pixels, which may include receiving a first image and a second image, the first image and the second images are of temporarily adjacent to each other and capture a same vehicle and background content. The method may also include obtaining a first bounding box that surrounds at least a part of the vehicle within the first image; wherein the first bounding has a first width and a first height; obtaining a second bounding box that surrounds at least the part of the vehicle within the second image; wherein the second bounding box has a second width and a second height; obtaining a mapping between pairs of initially matched pixels, wherein each pair comprises a first pixel and a second pixel that correspond to same entity portion, wherein the entity portion is a portion of the vehicle or of the background content. The method may also include determining, for each pair of at least some of the pairs, a first normalized relative location of a first pixel of the pair within the first bounding box and a second normalized relative location of a second pixel of the pair within the second bounding box; and determining for the each pair whether the pair is a vehicle related pair or a background content pair.