Adaptive Object Recognition Using Dynamic Feature Selection
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Solution Overview
Problem
Existing object recognition systems in driver assistance systems face challenges in reliably detecting objects under changing conditions such as varying brightness and angles of incidence, as they fail to adapt recognition criteria effectively.
Innovation Solution
The method involves selecting and applying different feature descriptions based on prevailing conditions to adapt object recognition criteria, using an integral image and a control unit with an interface for reading image information and situational parameters to select and search for feature combinations, enabling efficient object detection by focusing on relevant features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single fixed feature description is used for object recognition, then the system is simple to implement, but the recognition reliability deteriorates under changing conditions such as varying brightness and angles of incidence
Solution Approach 1:
The patent implements dynamic selection of feature descriptions based on detected environmental conditions. The system switches between different feature descriptions (e.g., contrast-based for daylight, reflector-based for nighttime) according to the prevailing lighting conditions, making the recognition system adaptive rather than static. This resolves the contradiction by allowing the system to maintain high reliability across varying conditions without requiring all possible feature descriptions to be active simultaneously.
Solution Approach 2:
The patent changes the parameters of the recognition system by selecting different feature descriptions based on environmental parameters such as brightness levels. Instead of using a single fixed set of recognition parameters, the system adjusts which feature description is applied based on detected conditions, thereby maintaining recognition reliability across different lighting scenarios while managing system complexity through conditional selection.
2Measurement precision
If multiple feature descriptions are maintained for different conditions, then the recognition accuracy improves under varying conditions, but the processing time increases due to the need to select and switch between different feature sets
Solution Approach 1:
The patent performs preliminary detection of environmental conditions (such as brightness levels) before executing the object recognition process. By determining the appropriate feature description in advance based on the detected condition, the system avoids time-consuming searches or switches during the actual recognition phase. This preliminary action ensures both high accuracy through appropriate feature selection and efficient processing by eliminating runtime decision overhead.
3Reliability
If all possible features are checked for every object, then the detection probability is maximized, but the processing speed decreases due to the increased number of checks required
Solution Approach 1:
The patent extracts and applies only the most relevant features for the current environmental condition rather than checking all possible features. For example, in daylight conditions, the system extracts and checks only contrast-based features, while in nighttime conditions, it extracts and checks only reflector-based features. This selective extraction maintains high detection probability for the relevant features while significantly reducing the number of checks required, thereby improving processing speed.
Data Source
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AI summary
The invention relates to a method for identifying an object in image information (104), wherein the image information (104) represents a scene (108) in an acquisition range of a camera (106) during a situation. The method includes a read-in step, a selection step (404) and a seeking step. In the read-in step, the image information (104) and at least one parameter (110) representing the situation are read in. In the selection step, a feature combination, which is expectably identifiable in the situation, of an object class of the object is selected using the parameter (110). In the seeking step, the feature combination is sought in the image information (104) in order to identify the object.