Vehicle Object Classification Using Specific Classifier Segmentation
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Solution Overview
Problem
Existing object recognition systems in vehicles rely solely on generic classifiers, which are inefficient in classifying unknown or difficult-to-recognize objects, leading to delayed recognition and poor tracking performance, especially for underrepresented object examples like small series vehicles or new generations.
Innovation Solution
A method and device that create a specific classifier using additional object features like color, structure, and form from camera images, allowing for rapid classification and adaptation, and enabling online learning to improve recognition accuracy and robustness by integrating a specific classifier alongside a generic classifier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If only a generic classifier is used for object recognition, then the system structure remains simple, but the recognition speed and accuracy for specific object types deteriorate
Solution Approach 1:
The classification system is segmented into two independent parts: a generic classifier for general object recognition and a specific classifier for specialized object types. This segmentation allows each classifier to operate independently with optimized features for its purpose, improving overall recognition speed without requiring a complete redesign of the classification architecture.
Solution Approach 2:
The generic classifier serves as a universal base that handles general object recognition across multiple object types. The specific classifier extends this universality by adding specialized recognition capabilities for particular object types, allowing the system to function effectively across a broad range of scenarios while maintaining simplicity for common cases.
2Measurement precision
If a generic classifier is used for all object types, then the system remains easy to operate, but the classification accuracy for unknown or difficult objects deteriorates
Solution Approach 1:
The specific classifier acts as an intermediary between the generic classifier and the final classification decision. It receives input from the generic classifier and refines the classification for specific object types, improving accuracy for difficult cases while maintaining the simplicity of the overall system through automated feature extraction and classifier integration.
3Measurement precision
If a specific classifier is created for each object type, then the classification accuracy improves, but the device complexity and computational requirements increase
Solution Approach 1:
Instead of creating a completely new classifier for each object type, the system applies local quality improvements by adding specific features and a specialized classifier component only where needed. The specific classifier focuses on extracting and utilizing object-specific features (such as color histograms, texture patterns, or shape characteristics) relevant to particular object types, rather than redesigning the entire classification system.
4Reliability
If only generic object features are used, then the system remains simple to implement, but the robustness in object tracking deteriorates
Solution Approach 1:
The system performs preliminary action by pre-defining and pre-processing specific object features (such as color histograms, texture descriptors, or shape parameters) before the actual classification and tracking processes. These pre-extracted features are stored and readily available for use by the specific classifier, improving tracking robustness without adding computational complexity during real-time operation.
Data Source
AI summary
A method for classifying an object for a vehicle. The method includes at least: classifying, extracting and creating. In the classifying, an object from a camera image is classified using a generic classifier. In the extracting, at least one specific object feature assigned to the object is extracted from the camera image. In the creating, a specific classifier is created using the specific object feature.