Multi-Dimensional Object Detection Using Line Segment Grouping
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Solution Overview
Problem
Existing vehicular navigation systems rely on expensive sensors that require high maintenance and struggle to distinguish between different types of obstacles, and number plate recognition is data-intensive and computationally complex, limiting real-time computation and effective multi-dimensional object detection.
Innovation Solution
A computer-implemented method and system for multi-dimensional object detection that processes video sequences to analyze line segments, group and merge them, detect objects using image classifiers, and apply optical character recognition for lane and obstacle detection, enabling timely and accurate recognition of road signage and obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor based approaches are used to detect obstacles, then obstacle detection capability is provided, but the system becomes expensive and requires high maintenance
Solution Approach 1:
The patent replaces expensive mechanical sensors with a vision-based system using standard cameras and image processing algorithms. The computer apparatus captures images and uses software-based line segment detection, grouping, and merging to identify obstacles, eliminating the need for costly sensor hardware while maintaining obstacle detection capability
Solution Approach 2:
The system creates a computational model of the road scene by detecting and processing image features (line segments) that represent physical objects. Instead of directly sensing physical properties with expensive sensors, the system creates and analyzes digital representations of the environment through image processing
2Reliability
If sensor based systems are used, then obstacle detection is achieved, but the system cannot distinguish between types of obstacles
Solution Approach 1:
The patent applies different image processing operations to different regions of the image based on local characteristics. Line segments are grouped and merged based on their spatial relationships and visual properties, allowing the system to distinguish between road markings, curbs, and obstacles by analyzing the local quality and arrangement of detected line segments in different image regions
Solution Approach 2:
The system segments the image into meaningful components by detecting individual line segments and then grouping them based on spatial proximity and orientation similarities. This segmentation approach allows different types of obstacles to be identified through their distinct geometric patterns and spatial arrangements
3Reliability
If number plate recognition with template matching is used, then car detection is achieved, but data handling requirements are large and real-time computation is limited
Solution Approach 1:
The patent extracts only the essential geometric features (line segments) from the image that are necessary for obstacle detection, rather than performing comprehensive template matching on entire number plates or objects. By extracting and processing only relevant line segment features, the system achieves car detection with minimal data handling and computational overhead
Solution Approach 2:
The system performs partial matching by detecting line segments and grouping them based on spatial and orientation criteria, rather than performing exhaustive template matching. This partial action approach provides sufficient information for real-time car detection without the computational burden of complete template matching
4Reliability
If geographic features are used for lane detection, then lane detection is achieved, but scaling to multiple lanes is computationally complex
Solution Approach 1:
The patent develops a universal line segment detection and grouping algorithm that can detect any number of lanes without requiring separate processing for each lane. The same computational operations (detect, group, merge) are applied universally to detect single or multiple lanes, making the system scalable without increasing computational complexity
Data Source
AI summary
Systems and methods for multi-dimensional object detection are described. Embodiments disclose receiving image frames, extracting image components in the image frame, identifying line segments in the extracted components, grouping the line segments into groups, based at least in part on one or more similarities between the slope associated with a line segment and the spatial proximity between the line segments, and merging each of the one or more identified line segments in a selected group into a single line segment. Embodiments additionally disclose detecting the position of one or more objects in the image frame by identifying objects in the image frame, producing a second version of the image frame, applying at least one image classifier to the image frame and the second version of the image frame, and identifying coordinates associated with at least one target object. Some embodiments additionally couple lane and object detection with alert generation.