Vehicle Interior Image Comparison for Left-Behind Item Detection
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Solution Overview
Problem
Existing vehicle systems lack effective methods to detect and alert occupants about items left behind in the vehicle after they exit, leading to potential loss or theft.
Innovation Solution
A method and system utilizing sensors to capture pre- and post-departure images of a vehicle's interior, processing these images through feature extraction and classification to identify and localize left-behind objects, and generating alerts to occupants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image processing and object detection algorithms are implemented in existing vehicle systems, then the ability to detect left-behind items is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system captures a background image of the vehicle interior before the passenger enters, establishing a reference state. This preliminary action enables subsequent comparison with post-departure images to efficiently detect left-behind items without requiring complex real-time analysis of all objects in the vehicle
Solution Approach 2:
The patent extracts and compares only the specific region or regions where items are likely to be left (such as seats or floor areas) between background and post-departure images. This selective extraction reduces computational complexity while maintaining detection accuracy for relevant objects
2Reliability
If comprehensive image processing is performed to accurately identify all left-behind objects, then detection reliability is improved, but processing time and energy consumption increase
Solution Approach 1:
The system performs image processing only on specific regions of interest within the vehicle interior where items are most likely to be left behind. This partial processing approach maintains detection reliability for critical areas while significantly reducing overall processing time and energy consumption compared to analyzing the entire vehicle interior
3Adaptability or versatility
If multiple sensors and processing steps are added to detect left-behind items, then detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The system uses existing vehicle sensors (cameras, sensors already present in the vehicle) for dual purposes: both for their original functions and for detecting left-behind items. This multi-functionality approach improves detection capability without requiring additional dedicated hardware, thereby avoiding increased device complexity and cost
Data Source
AI summary
An example operation includes one or more of capturing, by at least one sensor in a vehicle, a static background image of an interior of the vehicle before a passenger enters the vehicle, capturing, by the at least one sensor in the vehicle, a post-departure image of the interior of the vehicle after the passenger exits the vehicle, processing the static background image and the post-departure image through a feature extractor to generate feature maps, merging the feature maps to identify at least one difference between the post-departure image and the static background image, and determining an object left in the interior of the vehicle by analyzing the at least one difference using a classifier to identify a class of the object and a bounding box regressor to determine a position of the object within the vehicle.