Night Vehicle Detection Using Headlight Verification
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Solution Overview
Problem
Current traffic surveillance systems face challenges in detecting vehicles at night due to low light sensitivity and high noise levels in images, leading to false positives and missed detections, especially with common CCD or CMOS cameras which are prone to capturing noise and struggling with reflections and specularities.
Innovation Solution
A method that identifies candidate vehicle headlights based on luminance and verifies their presence through morphological operations and machine learning techniques, reducing false positives by confirming the presence of additional vehicle features like windshields, allowing for accurate detection and classification of vehicles in night-time scenes without requiring specific camera adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If common video CCD or CMOS cameras are used for night time vehicle detection, then the system cost is reduced, but the camera becomes more prone to capturing noise and struggling with reflections and specularities
Solution Approach 1:
The detection process is segmented into multiple stages: initial headlight candidate identification, pairing verification, and final vehicle confirmation. This multi-stage segmentation allows the system to process images systematically, reducing false positives while maintaining cost-effective CCD/CMOS camera usage.
Solution Approach 2:
The patent introduces an intermediary verification process that checks for additional vehicle features (such as vehicle body, windows, or other characteristic patterns) between headlight detection and final vehicle confirmation. This intermediary step filters out false positives from reflections and noise while preserving true vehicle detections.
2Reliability
If night vision sensors such as infrared-thermal cameras are used, then vehicle detection accuracy in night time scenes is improved, but the system cost becomes prohibitively costly
Solution Approach 1:
The patent employs inexpensive CCD or CMOS camera sensors instead of expensive infrared-thermal cameras. While these cameras have limitations in night time performance, the system compensates through sophisticated image processing algorithms that analyze luminance patterns, making the cheap camera sufficient for the application.
Solution Approach 2:
The system changes the analysis parameters by focusing specifically on luminance characteristics and spatial relationships of bright regions in night time images. By transforming the detection approach to rely on luminance ratios, spatial distributions, and temporal consistency rather than absolute brightness, the system achieves effective night time detection with standard cameras.
3Difficulty of detecting and measuring
If headlight detection is performed based on luminance in night time images, then vehicle detection capability is improved, but false positive identification of reflections as headlights increases
Solution Approach 1:
The patent combines multiple detection criteria: luminance thresholding, spatial relationship analysis between candidate headlights, temporal consistency checks across video frames, and verification of additional vehicle features. This merging of multiple criteria creates a robust detection system that reduces false positives while maintaining high detection capability.
Solution Approach 2:
The system performs preliminary filtering of headlight candidates based on luminance characteristics and spatial relationships before final vehicle confirmation. By pre-processing and filtering candidates early in the detection pipeline, the system eliminates many false positives before they reach the final detection stage, improving measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively reduces false positives and ensures no real vehicle pairs are missed, enabling accurate vehicle detection and classification in both lit and unlit night-time conditions using existing camera technology, without the need for expensive thermal cameras or multiple cameras, and can be applied to real-time traffic data extraction.
Implementation Method 1
the light sensitivity and contrast of the camera capturing the images is often too weak. Further the moving reflections and specularities of head lights as captured in images of night time scenes
Data Source
AI summary
The invention concerns the detection of vehicles in images of a night time scene. In particular, but not limited to, the invention concerns a traffic surveillance system that is used to detect and track vehicles to determine information about the detected and tracked vehicles. Candidate pair of headlights are identified 900 in an image based on luminance of points in the image. These candidates are then verified 902 by identifying 400i a sub-image of the image sized to include a candidate vehicle having the pair of candidate headlights; and determining whether the candidate vehicle is a vehicle represented in the image by testing 400k the sub-image for the presence of predetermined features of a vehicle other than the headlights. Aspects of the invention include a method, software and computer hardware.