Vehicle Interior Item Detection Using Depth Background Removal
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Solution Overview
Problem
The challenge is to efficiently detect forgotten items within vehicles and promptly notify occupants or emergency services, especially in autonomous vehicle scenarios where human monitoring is limited.
Innovation Solution
The solution involves a method that captures initial and final images of a vehicle interior during a trip, using depth metrics and background removal techniques to identify remaining elements. These elements are then categorized, and based on their type, notifications are selectively sent to the vehicle occupants or emergency services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional image processing methods are used to detect forgotten items, then the detection process becomes complex and time-consuming, but using depth aided background removal improves detection efficiency and accuracy
Solution Approach 1:
The patent introduces depth information (Z-axis) to complement traditional 2D RGB images, creating 3D RGB-D images. This dimensional enhancement allows the system to differentiate between background elements and forgotten items more effectively by analyzing depth disparities, thereby improving detection efficiency without significantly increasing processing complexity
Solution Approach 2:
The patent employs depth maps as an intermediary data structure that facilitates the comparison between current and historical images. The depth information acts as a mediator that highlights changes in the vehicle interior by removing static background elements, enabling faster and more accurate detection of forgotten items
2Measurement precision
If comprehensive monitoring of vehicle interior is implemented to detect all forgotten items, then detection accuracy improves, but the system requires more extensive processing resources and time
Solution Approach 1:
The patent captures and stores historical images of the vehicle interior at the beginning of trips or when the vehicle is empty. These pre-captured images serve as reference backgrounds that are reused for multiple detection operations, eliminating the need to re-process entire interior scenes repeatedly and reducing processing time while maintaining detection accuracy
Solution Approach 2:
The patent extracts and removes static background elements from the vehicle interior by comparing current images with historical references. This extraction process isolates only the changed elements (potential forgotten items), significantly reducing the amount of data that requires detailed analysis and thereby decreasing processing time while preserving detection accuracy
3Reliability
If depth sensors and multiple cameras are deployed to improve detection capability, then detection reliability increases, but the device complexity and cost increase
Solution Approach 1:
The patent designs the image processing system to handle both RGB and depth data using unified processing pipelines and algorithms. The same computational framework processes both traditional color images and depth-enhanced images, allowing the system to achieve improved detection reliability through multi-functional processing without requiring separate dedicated systems for each sensor type
Data Source
AI summary
Vehicle forgotten item detection using depth aided image background removal includes determining a first image of an interior of a vehicle for a start of a time-period and a second image for the interior of the vehicle for an end of the time-period. The first image and the second image include a depth metric. One or more elements present in both the first image and the second image are removed from the second image. One or more remaining elements are determined to exist within the second image after removal, which are categorized. An occupant of the vehicle is selectively notified based on the categorization.