Vehicle Seat Belt Detection via Front View Image Feature Extraction
Solution Overview
Problem
Current methods for detecting seat belt violations in vehicles are inefficient and have a low detection rate, relying on visual examination by police officers or remote video monitoring, which are costly and ineffective.
Innovation Solution
A system and method that utilize image processing techniques to extract features from vehicle images, comparing them to trained classifiers to determine if a seat belt is buckled, involving image capture, feature extraction, and classification using machine learning algorithms to identify seat belt status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If police officers or remote video monitoring are used to detect seat belt violations, then enforcement capability is provided, but cost increases and detection rate remains low
Solution Approach 1:
The patent replaces manual visual inspection by police officers and complex remote video monitoring systems with an automated image processing system that uses computer algorithms to detect seat belt status. The system captures images through the windshield, extracts features related to seat belt buckles, and automatically determines compliance, eliminating the need for human officers to physically stop vehicles for inspection.
Solution Approach 2:
The system enables self-service detection by having the vehicle's own image capture device and processing system identify seat belt violations automatically. The image processing algorithm independently analyzes captured images, extracts relevant features, and determines seat belt status without requiring external enforcement intervention, making the detection process autonomous and continuous.
2Productivity
If manual visual examination by police officers is used, then seat belt violations can be detected, but time consumption and cost increase
Solution Approach 1:
The system enables continuous detection of seat belt violations by automatically processing images as they are captured, without interruption or manual intervention. The image processing algorithm continuously extracts features and determines seat belt status in real-time, allowing for ongoing monitoring of multiple vehicles simultaneously, thereby dramatically increasing detection efficiency compared to discrete manual inspections.
3Measurement precision
If remote video monitoring is implemented, then enforcement capability is provided, but detection accuracy decreases
Solution Approach 1:
The system extracts only the critical features necessary for seat belt detection from captured images, such as buckle presence, strap positioning, and geometric relationships. By focusing on these specific extracted features rather than analyzing entire video feeds manually, the system achieves high detection accuracy while maintaining relatively simple processing requirements.
Data Source
AI summary
A system and method for detecting a seat belt violation in a vehicle. The method may include receiving an image including a region of interest where a seat belt is visible if the seat belt is buckled. One or more first features may be extracted from the image. The one or more first features may be compared to one or more second features extracted from a plurality of sample images. It may be determined whether the seat belt is buckled in the image based upon the comparison.