Location-dependent pedestrian detection dictionaries
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Solution Overview
Problem
Pedestrian detection systems using vehicular cameras often fail to accurately detect pedestrians due to variations in clothing, accessories, and background items specific to different countries or areas, leading to non-detection and erroneous detection.
Innovation Solution
A vehicle equipped with a camera module that selects and uses dictionary information specific to the detected area, allowing for high-accuracy pedestrian detection by switching between different dictionary sets based on geographic location, thereby reducing errors caused by regional variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single pattern matching dictionary is used for pedestrian detection, then the detection method is simple and device complexity is low, but detection accuracy deteriorates when pedestrians have features different from learned features or when background items vary by region
Solution Approach 1:
The patent segments the single pattern matching dictionary into multiple region-specific dictionaries (first dictionary for first region, second dictionary for second region, etc.). Each dictionary is trained on pedestrian data specific to its region, allowing the system to adapt to local variations in pedestrian appearance and background characteristics. This segmentation resolves the contradiction by improving detection accuracy through regional specialization while maintaining manageable complexity through modular organization.
Solution Approach 2:
The patent applies local quality by making the dictionary information region-specific rather than uniform. Each region has its own optimized dictionary that reflects local pedestrian features and background characteristics. This allows the detection system to have high accuracy locally in each region while the overall system handles multiple regions through selective dictionary application based on GPS coordinates.
2Reliability
If pattern matching is performed without considering regional characteristics, then the detection process is fast and simple, but non-detection and erroneous detection occur frequently in region-specific conditions
Solution Approach 1:
The patent performs preliminary action by pre-dividing the service area into multiple regions and pre-training separate pattern matching dictionaries for each region before deployment. The system uses GPS coordinates to determine the current region and selects the appropriate pre-trained dictionary for detection. This preliminary preparation improves detection reliability by ensuring the right dictionary is used for each region, while the automation of dictionary selection keeps the operational complexity manageable.
3Measurement precision
If multiple region-specific dictionaries are stored and switched based on GPS coordinates, then detection accuracy for region-specific pedestrians is improved, but the quantity of stored data and processing complexity increases
Solution Approach 1:
The patent applies universality by designing a detection system that can function across multiple regions using a standardized framework. The same camera module, processing unit, and control logic handle detection in all regions, differing only in the selected dictionary. This multi-functional design allows the system to adapt to different regions without requiring completely separate detection systems for each area, thereby managing data quantity and processing complexity efficiently.
Data Source
AI summary
A camera module includes a camera imaging a region outside a rear end portion of a vehicle and a storage storing first and second dictionary information corresponding to a first area and a second area. When the camera take an image of a pedestrian and a detected latitude and longitude correspond to the first area, the camera module recognizes the image of the pedestrian based on the first dictionary information, outputs a first vehicle control signal based on a recognition result, and outputs a status the first dictionary information is used. When the camera takes an image of a pedestrian and a detected latitude and longitude correspond to the second area, the camera module recognizes the image of the pedestrian based on the second dictionary information, outputs a second vehicle control signal based on a recognition result, and outputs a status that the second dictionary information is used.