Vehicle Camera Object Detection Using Single Dictionary and Overlap Analysis
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Solution Overview
Problem
Existing image processing technologies for onboard vehicle cameras face challenges in accurately detecting objects with changing shapes, leading to erroneous detections and requiring large memory and longer calculation times due to the need for multiple dictionaries and complex processing stages.
Innovation Solution
An image processing apparatus with a single dictionary generated using images of objects at various positions, allowing for accurate detection by determining overlapping regions of position candidate frames and reducing erroneous detections through a final detection unit that utilizes a count grid and frame grid processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple dictionaries are used for detection processing, then detection accuracy is improved, but memory size and processing time increase
Solution Approach 1:
The patent segments the detection process into multiple stages (first detection processing stage with first dictionary, second detection processing stage with second dictionary) rather than using all dictionaries simultaneously. This allows the system to achieve high detection accuracy through sequential processing while keeping memory requirements manageable by loading only one dictionary at a time.
Solution Approach 2:
The patent performs preliminary detection using a first dictionary to identify candidate objects, then uses a second dictionary for confirmation. This preliminary action approach allows the system to filter out obvious non-matches early, reducing the need for large memory allocations for all possible detection scenarios.
2Measurement precision
If multiple dictionaries are used for detection processing, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The detection process is divided into sequential stages where each stage uses a specific dictionary for a particular purpose. The first stage uses a comprehensive dictionary for initial detection, while the second stage uses a specialized dictionary for confirmation. This segmentation allows the system to achieve high accuracy without the computational overhead of simultaneously processing all dictionaries.
Solution Approach 2:
The patent applies partial action by using different dictionaries for different detection needs - the first dictionary is used for broad initial screening, and the second dictionary is applied only to candidate regions identified in the first stage. This selective application reduces total processing time compared to applying all dictionaries to the entire image.
3Productivity
If a single dictionary is used, then memory size and processing time are reduced, but detection accuracy deteriorates when object shape changes
Solution Approach 1:
The patent implements a dynamic detection system that adapts the dictionary selection and processing stage based on the detection context. When object shapes vary, the system can switch between different dictionaries and processing stages appropriately, maintaining high detection accuracy while preserving processing efficiency through intelligent resource allocation.
Solution Approach 2:
The patent changes detection parameters (dictionary selection, processing stage) based on the detection scenario and object characteristics. When object shapes change, the system adjusts which dictionary to use and which processing stage to apply, thereby maintaining detection accuracy without sacrificing processing efficiency that would result from always using the most comprehensive approach.
4Reliability
If multiple detection stages are implemented, then erroneous detection is reduced, but processing complexity increases
Solution Approach 1:
The patent segments detection into distinct stages (first detection processing stage, second detection processing stage), each with specific dictionaries and processing logic. This segmentation reduces erroneous detection by allowing each stage to focus on specific detection aspects, while the modular structure actually simplifies overall system management compared to a monolithic complex detection system.
Solution Approach 2:
The patent introduces intermediate processing steps between initial detection and final confirmation. These intermediary stages act as filters that reduce erroneous detections while maintaining a manageable processing complexity through clear, defined transition criteria between stages.
Data Source
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AI summary
According to one embodiment, an image processing apparatus and an image processing method includes a processed image generation unit, a provisional detection unit, and a final detection unit. The processed image generation unit generates a processed image for detection processing from an image around a vehicle taken by a camera provided on the vehicle. The provisional detection unit scans a scanning frame of a predetermined size according to a detection target object on the processed image, and detects a plurality of position candidate regions of the detection target object within the processed image by determining a feature value for respective scanning positions using a dictionary of the detection target object. The final detection unit determines an overlapping region of the plurality of position candidate regions, and determines a final detection position of the detection target object within the processed image based on the overlapping region and the plurality of position candidate regions.