Multi-lens Camera Optical Filter Segmentation for Recognition Accuracy
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Solution Overview
Problem
Conventional in-vehicle stereo camera systems face challenges in complex city driving environments, where diverse sensing conditions lead to increased processing time and object misrecognition due to the complexity of recognizing various targets like vehicles, pedestrians, traffic signs, and traffic lights, especially when using parallax information for clustering and recognition.
Innovation Solution
A multi-lens camera system with multiple camera units and optical filters, each divided into regions with different optical characteristics, allows for targeted parallax calculation and image processing, using luminance and color information effectively to improve recognition accuracy and reduce misrecognition by dividing images into regions suitable for specific recognition tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If parallax calculation and clustering are executed over the entire captured image using conventional stereo camera systems, then comprehensive object detection is achieved, but processing time lengthens and object misrecognition increases in complex city driving environments
Solution Approach 1:
The captured image is divided into multiple regions of interest (ROIs) based on optical filter characteristics. Parallax calculation and clustering are executed separately for each ROI rather than the entire image. This segmentation reduces the computational burden and processing time while maintaining comprehensive object detection across different spatial zones with diverse sensing conditions.
Solution Approach 2:
Different optical filters are assigned to different regions of the image to optimize detection for local characteristics. Each ROI receives processing tailored to its specific optical characteristics and sensing conditions, improving recognition accuracy for local objects while reducing overall processing complexity.
2Adaptability or versatility
If diverse sensing conditions in complex environments are processed using conventional uniform algorithms, then all object types are targeted, but object misrecognition increases due to processing complexity
Solution Approach 1:
The system assigns different optical filters to different regions to match local sensing conditions. Each ROI is processed with algorithms optimized for its specific characteristics, improving recognition reliability for diverse object types including vehicles, pedestrians, traffic signs, and traffic lights without uniform processing complexity.
Solution Approach 2:
The image is segmented into multiple ROIs with distinct optical characteristics. This allows the system to apply specialized processing to each segment, enhancing adaptability to diverse targets while reducing misrecognition through localized optimization rather than uniform complex processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances the recognition success rate by optimizing parallax calculation and image processing, reducing processing time and costs, and enabling accurate detection of various targets in complex environments without the need for additional sensors or complex algorithms.
Implementation Method 1
each of the optical filters (12, 22) has multiple filter regions (12a, 12b, 12c; 22a, 22b, 22c) whose optical characteristics differ respectively
Data Source
Figure 1A~1B
Figure 2~3
Figure 4
AI summary
A multi-lens camera system (1000) includes multiple camera units (10, 20) including respective optical filters (12, 22) and image sensors (11, 21) that acquire captured images (40) via the optical filter (12,22), each of the optical filters (12, 22) having multiple filter regions (12a, 12b, 12c; 22a, 22b, and 22c) whose optical characteristics differ respectively, and an image processor (30), operably connected to the multiple camera unit (10, 20), to execute different types of image processing on the captured image (10A, 20A, 40) to form an image (40) that contains multiple image regions (40a, 40b, 40c) whose characteristics differ depending on the optical characteristics of the filter region (12a, 12b, 12c; 22a, 22b, 22c) in which they are acquired.