Front Seat Occupancy Detection via Edge Pattern Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current systems for High Occupancy Vehicle (HOV) lane enforcement face challenges in automatically determining the number of occupants, particularly for front seats, leading to inefficiencies in enforcement and increased workload for authorities.
Innovation Solution
A method that captures and processes images of motor vehicle seats using edge detection and uniformity analysis to differentiate between occupied and unoccupied seats, combined with face detection to verify passenger presence, and alerts authorities of potential HOV lane violations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If entirely automatic machine detection is used to determine occupant number, then productivity of enforcement authorities is improved, but detection accuracy and reliability deteriorate due to difficulty in detecting back seat passengers
Solution Approach 1:
The patent implements a semi-automatic detection system that performs partial automation for front seat occupancy detection while allowing human verification for back seat passengers. This approach achieves sufficient automation for the easier-to-detect front seats without requiring complete automation that would be needed for reliable back seat detection, thus improving productivity while maintaining acceptable reliability through selective automation.
Solution Approach 2:
The patent introduces an intermediary semi-automatic system that bridges entirely manual detection and fully automatic detection. The machine detects front seat occupancy and generates alerts for verification, serving as an intermediary step that reduces human workload while maintaining reliability through human oversight for complex cases like back seat detection.
2Reliability
If semi-automatic method with human verification is used, then detection reliability is improved, but productivity of enforcement authorities deteriorates due to increased human workload
Solution Approach 1:
The patent applies partial automation by implementing automatic detection only for front seat passengers where detection is more reliable, while requiring human verification only for back seat passengers. This selective approach improves productivity by automating the majority of detection cases while maintaining reliability through human oversight only when necessary.
Solution Approach 2:
The system performs self-service detection for front seat occupancy using automatic image analysis, eliminating the need for human verification in these cases. This self-service capability handles the majority of detection cases automatically, improving productivity while human operators only need to verify complex back seat cases.
3Reliability
If entirely human detection is used, then detection reliability is maintained, but productivity of enforcement authorities deteriorates significantly due to high workload
Solution Approach 1:
The patent implements self-service automatic detection for front seat occupancy, where the system independently analyzes images and determines occupancy without human intervention. This eliminates manual workload for front seat detection while maintaining reliability through automated algorithms, significantly improving productivity compared to entirely manual detection.
Solution Approach 2:
The patent replaces the mechanical human detection process with an automated image analysis system for front seat occupancy. The machine vision system substitutes human operators for the specific task of detecting front seat passengers, improving productivity while maintaining detection reliability through consistent automated analysis.
Data Source
AI summary
What is disclosed is a system and method for determining whether a front seat in a motor vehicle is occupied based on seat pattern recognition. The present invention takes advantage of the observation that an unoccupied seat of a motor vehicle exhibits features which are distinguishable from an occupied seat. An unoccupied motor vehicle seat typically features long contiguous horizontal line segments and curve segments, and substantially uniform areas encompassed by these segments which are not present in an occupied seat. The present method provides a long horizontal edge test which uses location information within a defined window of the image, edge linking, softness of the edge, number of lines, line/curve fitting, and other techniques to locate horizontal edges in the image which define a seat, and a uniformity step which determines whether the area bounded by the horizontal edges is relatively uniform indicating an unoccupied seat.