Wrong-Way Vehicle Detection Using Image and Histogram Analysis
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Solution Overview
Problem
Existing systems struggle to accurately detect and prevent wrong-way driving, which is particularly dangerous on highways due to high speeds, often resulting from driver error or confusion with traffic directions.
Innovation Solution
An apparatus and method utilizing object image analysis and color histogram analysis, employing a combination of learned object detection, classification, and anomaly detection models to determine if a vehicle is driving the wrong way, incorporating learning processes to optimize model weights and thresholds for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods are used, then system complexity is low, but detection accuracy is insufficient
Solution Approach 1:
The patent combines multiple detection approaches (object detection model, classification model, and anomaly detection model) into a unified wrong-way driving detection system. The object detection model identifies vehicles, the classification model determines driving direction, and the anomaly detection model identifies abnormal patterns, merging these functions to achieve high detection accuracy while managing system complexity through integrated architecture.
Solution Approach 2:
The detection system is segmented into distinct functional modules: an object detection model for vehicle identification, a classification model for direction determination, and an anomaly detection model for pattern recognition. Each module processes specific aspects of the data independently before integrating results, allowing for improved accuracy while maintaining manageable complexity through modular design.
2Measurement precision
If multiple detection models are employed, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary object detection and bounding box extraction before classification and anomaly detection. By pre-identifying vehicles and their locations using the object detection model, the subsequent classification and anomaly detection processes only need to analyze relevant regions, reducing overall processing time while maintaining high accuracy through the multi-model approach.
Solution Approach 2:
The system applies different levels of detection intensity to different aspects: the object detection model provides comprehensive vehicle identification, while the classification and anomaly detection models focus specifically on directional analysis. This partial application of detection resources to critical aspects optimizes the balance between accuracy and processing time.
Data Source
AI summary
Embodiments relate to an apparatus and method for detecting or tracking a wrong-way driving vehicle based on object image analysis or color histogram analysis. The detection or tracking of a wrong-way driving vehicle uses at least one of an object detection model, a classification model, and an anomaly detection model.