Hybrid Dynamic Bayesian Network for Real-Time Vehicle Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle classification systems are inadequate for robust and complete classification of vehicles from a rear-side view, lacking real-time capability and color inference, and are slow, especially when only the license plate recognition stage operates in real-time.
Innovation Solution
A stochastic multi-class vehicle classification system using a Hybrid Dynamic Bayesian Network (HDBN) that processes a feature vector derived from low-level tail light and vehicle dimension features extracted from rear views, eliminating the need for high-resolution images and enabling real-time classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional vehicle classification systems use complex features and classifiers, then classification accuracy may be improved, but processing speed deteriorates and real-time capability is lost
Solution Approach 1:
The patent extracts only the most discriminative features (tail light characteristics and vehicle dimensions) from the full image, eliminating the need to process entire high-resolution images. This selective extraction of critical features maintains classification accuracy while dramatically reducing computational load and enabling real-time processing.
Solution Approach 2:
The patent employs computationally inexpensive features (simple geometric and color properties) instead of complex deep learning features. These simple features can be computed rapidly from low-resolution images, providing a disposable, low-cost approach that achieves real-time performance without sacrificing too much accuracy.
2Measurement precision
If high-resolution images are used for vehicle classification, then feature extraction accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts only the most discriminative features (tail light characteristics and vehicle dimensions) from the full image, eliminating the need to process entire high-resolution images. This selective extraction of critical features maintains classification accuracy while dramatically reducing computational load and enabling real-time processing.
Solution Approach 2:
The patent uses low-resolution images rather than full high-resolution images, accepting partial information (only the rear view at reduced quality) to achieve the classification task. This partial action approach reduces computational complexity significantly while still providing sufficient information for accurate vehicle classification.
3Reliability
If comprehensive vehicle features are analyzed, then classification robustness is improved, but processing time increases
Solution Approach 1:
The patent extracts only the most discriminative features (tail light characteristics and vehicle dimensions) from the full image, eliminating the need to process entire high-resolution images. This selective extraction of critical features maintains classification accuracy while dramatically reducing computational load and enabling real-time processing.
Solution Approach 2:
The patent performs feature extraction and classification on simplified, low-resolution images first, obtaining preliminary classification results rapidly. This preliminary action provides timely classification without the time cost of processing full-resolution comprehensive features.
Data Source
AI summary
A system and method for classification of passenger vehicles and measuring their properties, and more particularly to a stochastic multi-class vehicle classification system, which classifies a vehicle (given its direct rear-side view) into one of four classes Sedan, Pickup truck, SUV/Minivan, and unknown, and wherein a feature pool of tail light and vehicle dimensions is extracted which feeds a feature selection algorithm to define a low-dimensional feature vector, and the feature vector is then processed by a Hybrid Dynamic Bayesian Network (HDBN) to classify each vehicle.