Vehicle Detection System Using Dynamic ROI for Low Light Accuracy
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Solution Overview
Problem
Existing vehicle detection systems are inefficient and costly, particularly in low light conditions, and struggle to accurately differentiate between vehicles and false objects like street lights or traffic cones.
Innovation Solution
A vehicle detection system comprising a scene recognition module, road topology estimation module, and vehicle detecting module that processes high and low exposure images to identify vehicles by segmenting light sources, removing noise, and validating blob pairs based on specific criteria, thereby enhancing accuracy in low light conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional visual processing systems are used for vehicle detection, then the system can operate in various lighting conditions, but the detection accuracy deteriorates in low light conditions and the system produces false positives from objects like street lights and traffic cones
Solution Approach 1:
The patent segments the image processing task into multiple specialized modules: scene recognition module identifies lighting conditions (day/night), road topology estimation module determines road geometry (curve, slope, vanishing point), and vehicle detecting module performs actual vehicle detection. This segmentation allows each module to optimize for its specific function, improving overall detection accuracy while reducing false positives from non-vehicle light sources.
Solution Approach 2:
The patent applies local quality by creating a dynamically varying region of interest (ROI) based on road topology and scene conditions. Instead of processing the entire image uniformly, the system focuses computational resources on the specific ROI where vehicles are likely to appear, adjusting the ROI boundaries based on curve, slope, and vanishing point calculations. This localized approach improves detection precision while reducing false positives from irrelevant areas.
2Productivity
If existing vehicle detection algorithms are used, then the system can detect vehicles in day time, but the detection efficiency and accuracy deteriorate in night time conditions
Solution Approach 1:
The patent implements dynamics by making the ROI (region of interest) dynamically varying based on real-time scene recognition and road topology estimation. The ROI boundaries are continuously adjusted according to detected curve, slope, and vanishing point parameters, allowing the system to adapt to changing road conditions and lighting scenarios. This dynamic adaptation maintains high detection efficiency across varying conditions, particularly improving night-time performance.
Solution Approach 2:
The patent changes detection parameters based on scene recognition results. When the scene is identified as night-time conditions, the system adjusts its detection parameters and focuses on specific ROI areas determined by road topology. This parameter adaptation allows the system to maintain high detection accuracy in low-light conditions by optimizing for the specific environmental context.
3Measurement precision
If the system processes the entire image for vehicle detection, then comprehensive detection is possible, but the processing complexity and computational cost increase
Solution Approach 1:
The patent extracts and processes only the relevant portion of the image by defining a dynamically varying ROI based on road topology estimation. Instead of analyzing the entire image, the system extracts the specific region where vehicles are likely to appear (between the vanishing point and camera, within lane boundaries) and focuses computational resources there. This extraction approach maintains comprehensive detection coverage while significantly reducing processing complexity.
Data Source
AI summary
The present invention describes a vehicle detection system and method for detecting one or more vehicles in a dynamic varying region of interest, ROI. The system comprises a scene recognition module (101), a road topology estimation module (102), and a vehicle detecting module (103). The scene recognition module is configured for receiving either high exposure image or low exposure image for identifying condition of one or more scenes in a dynamically varying region of interest. The road topology estimation module configured for receiving either high exposure image or low exposure image for determining at least one of a curve, slope and vanishing point of a road in the dynamically varying region of interest. The vehicle detecting module is coupled with the scene recognition module and road topology module for detecting one or more vehicles on the road at night time.