Sensor-Triggered Image Capture for License Plate Recognition
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Solution Overview
Problem
Existing law enforcement systems struggle to efficiently capture and process still images of regions-of-interest, such as vehicle license plates, during video streaming, especially under challenging conditions, which hinders efficient law enforcement operations.
Innovation Solution
A computer-implemented method and network server system that captures still images during video streaming using sensors and algorithms to identify regions-of-interest, applying optical character recognition (OCR) to extract patterns like license plate characters, with event detection models to trigger image capture and enhance resolution as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If still images are captured continuously during video streaming to ensure pattern identification, then identification accuracy is improved, but data transmission load and processing time increase
Solution Approach 1:
The system performs preliminary actions by continuously capturing still images during video streaming before pattern identification is needed. This ensures that high-quality images are already available when pattern identification is triggered, eliminating the need for delayed capture and reducing overall processing time.
Solution Approach 2:
The system uses self-service by automatically triggering pattern identification on captured still images without requiring manual intervention. The event detection model autonomously determines when pattern identification should occur, and the system automatically processes the images, reducing both human workload and processing delays.
2Measurement precision
If high resolution still images are captured for pattern identification, then identification accuracy is improved, but bandwidth consumption and storage requirements increase
Solution Approach 1:
The system extracts only the essential information from high-resolution still images for pattern identification, such as license plate regions and key visual features. By extracting and transmitting only the critical data elements rather than entire high-resolution images, the system maintains identification accuracy while significantly reducing data transmission volume.
Solution Approach 2:
The system applies local quality by capturing high-resolution images only in specific regions of interest (such as license plate areas) rather than uniformly high resolution across the entire image. This allows the system to maintain high identification accuracy for critical elements while reducing overall data transmission requirements.
3Measurement precision
If manual review of captured images is required for pattern identification, then accuracy is improved, but operational efficiency decreases
Solution Approach 1:
The system implements self-service through automated pattern identification using event detection models and OCR technology. The system autonomously captures still images, identifies patterns such as license plate characters, and generates identification results without requiring manual review, thereby maintaining high accuracy while dramatically improving operational efficiency.
Solution Approach 2:
The system replaces the mechanical process of manual image review with automated computational methods including event detection models and optical character recognition (OCR). This substitution eliminates human intervention in the pattern identification process, maintaining or improving accuracy while significantly increasing productivity and operational efficiency.
Data Source
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AI summary
This disclosure describes techniques for capturing still images during video streaming to identify patterns in a region-of-interest on the captured still images. The video streaming may be performed by an imaging device that includes or is communicatively connected to one or more sensors (e.g., radar, light sensor, etc.) The one or more sensors may be configured to perform data measurements such as vehicle speed measurements, light intensity measurements, and/or the like. In one example, during the video streaming, the data measurement may be compared with a corresponding threshold. In this example, the imaging device may be triggered to capture still images of the surrounding area based on the comparison between the data measurement and the corresponding threshold. Thereafter, the still images may be processed to identify the region-of-interest on the still images.