Vehicle Periphery Monitoring with Clustered Object Identification
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Solution Overview
Problem
Existing vehicle periphery monitoring systems face challenges in accurately detecting object positions due to the extraction of multiple area candidates with slight variations, leading to unnecessary computations and reduced efficiency in the object identification process.
Innovation Solution
A vehicle periphery monitoring device that employs a two-tiered identification process, using a first identifying unit with low computation requirements for initial object detection and a second unit with higher computation requirements for detailed identification, combined with a clustering process to classify and prioritize identification target areas based on position and scale, thereby reducing overall computation and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a simple primary identification process is performed to extract area candidates, then the computation time is reduced, but the position accuracy of detected objects deteriorates
Solution Approach 1:
The patent segments the identification process into two distinct stages: a primary identification process that quickly extracts area candidates with rough position information, and a secondary identification process that performs detailed analysis only on clustered candidates. This segmentation allows the system to achieve fast initial detection while maintaining accuracy through selective detailed processing.
Solution Approach 2:
The patent performs preliminary clustering of area candidates based on position and scale before conducting the computationally intensive secondary identification. This preliminary action groups multiple candidates representing the same object, so that subsequent detailed identification is performed only once per object rather than once per candidate, reducing unnecessary computations while maintaining position accuracy.
2Reliability
If multiple area candidates with slight position variations are extracted, then the detection coverage is improved, but unnecessary computations increase
Solution Approach 1:
The patent merges multiple area candidates that represent the same object by performing clustering based on position and scale similarity. Candidates that are close in position and scale are grouped together, and only one representative candidate from each group undergoes the computationally intensive secondary identification process. This merging maintains detection coverage while eliminating redundant computations.
Solution Approach 2:
The patent changes the approach from processing each candidate individually to processing clustered groups of candidates. By introducing clustering parameters (position-based clustering and scale-based classification), the system transforms the problem from O(n) secondary identifications to O(m) where m < n, improving computation efficiency while maintaining detection reliability.
3Measurement precision
If a detailed secondary identification process is applied to each area candidate, then the object identification accuracy is improved, but the computation amount increases
Solution Approach 1:
The patent applies the detailed secondary identification process only to selected area candidates that pass the clustering filter, rather than to all extracted candidates. This partial action approach ensures that detailed identification is performed only where necessary (on representative candidates from each cluster), maintaining high identification accuracy while reducing the total computation amount by eliminating redundant processing of similar candidates.
Data Source
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AI summary
The present invention relates to a vehicle periphery monitoring device. An object identification unit (42) comprises: a first identifier (50) requiring a relatively low computation volume for an object identification process; and a second identifier (52) requiring a relatively high computation volume for the object identification process. A region to be identified determination unit (40) determines at least one region to be identified (95-99, 105-107, 112, 113) which is presented in the identification process by the second identifier (52), by carrying out a clustering process relating to location and/or scale, with respect to a plurality of region candidates (74) which are extracted by the first identifier (50) as wherein objects (66, 68) are present.