Street-Level Face and License Plate Detection Using 3D Road Width Features
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Solution Overview
Problem
Current image processing technologies face challenges in reliably detecting and obscuring faces and license plates in street-level imagery due to variations in image capture conditions, leading to high false positive rates and incomplete detection, which compromises privacy and image quality.
Innovation Solution
A two-stage process using machine learning algorithms, where a primary high-recall sliding window detector identifies candidate objects, and a secondary high-precision low sensitivity detector filters out false positives with the aid of 3D road width data and application-specific cues to generate accurate feature vectors for classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a high-recall object detection algorithm is used to detect faces and license plates, then the detection coverage is improved, but the false positive rate increases
Solution Approach 1:
The detection system is divided into two independent stages: a first stage detector that operates with high recall to ensure comprehensive detection coverage, and a second stage classifier that operates with high precision to filter false positives. This segmentation allows each component to be optimized for its specific function without compromising the other.
Solution Approach 2:
A road context model serves as an intermediary between the first stage detector and second stage classifier. This intermediary uses 3-D road width features and contextual information to bridge the gap between high-recall detection and high-precision filtering, enabling the system to leverage contextual cues for more accurate classification.
2Productivity
If traditional object detection algorithms are used without contextual information, then the processing speed is maintained, but the detection accuracy deteriorates
Solution Approach 1:
The system performs preliminary action by pre-computing and storing road context models that include 3-D road width features, sidewalk locations, and other contextual information before the actual detection process. This pre-computation allows the detection algorithm to quickly access relevant contextual cues during runtime without performing complex computations in real-time, thus maintaining processing speed while improving accuracy.
3Measurement precision
If more contextual features are added to the detection algorithm, then the detection accuracy is improved, but the computational complexity increases
Solution Approach 1:
The system applies local quality by selectively incorporating contextual features that are most relevant to specific detection scenarios. Rather than uniformly adding all possible contextual information, the road context model focuses on locally relevant features such as 3-D road width at the specific location, adjacent sidewalks, and nearby objects, reducing unnecessary computational overhead while maintaining detection accuracy.
Data Source
AI summary
A computer implemented system for identifying license plates and faces in street-level images is disclosed. The system includes an object detector configured to determine a set of candidate objects in the image, a feature vector module configured to generate a set of feature vectors using the object detector to generate a feature vector for each candidate object in the set of candidate objects, a composite feature vector module to generate a set of composite feature vectors by combining each generated feature vector with a corresponding road or street description of the object in question, and an identifier module configured to identify objects of a particular type using a classifier that takes a set of composite feature vectors as input and returns a list of candidate objects that are classified as being of the particular type as output.