In-Vehicle Item Detection Using Cameras and Neural Networks
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Solution Overview
Problem
Existing vehicle systems lack an effective method to detect and alert users about items left behind in the vehicle upon exiting, which can lead to safety issues and property loss.
Innovation Solution
A vehicle system equipped with cameras and an artificial neural network (ANN) that monitors the user and items, detects when an item is brought into the vehicle, and generates alerts when the item is left behind after the user exits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a computing system with sensors and neural networks is implemented to detect forgotten items, then detection capability is improved, but device complexity increases
Solution Approach 1:
The computing system performs multiple functions: it processes sensor data for autonomous driving, analyzes images from cameras to identify items, tracks user movements, and generates alerts for forgotten items. By making the computing system universal and multi-functional, the patent avoids adding separate dedicated detection systems, thereby improving detection capability without proportionally increasing overall system complexity
Solution Approach 2:
The system uses its existing autonomous driving sensors and neural network infrastructure to simultaneously perform forgotten item detection. The same cameras and processors used for navigation and safety also identify items left in the vehicle, allowing the system to serve itself for multiple purposes without requiring entirely separate detection hardware
2Measurement precision
If cameras and neural networks are used to monitor items, then detection accuracy is improved, but energy consumption increases
Solution Approach 1:
The system performs detection at specific periodic intervals: scanning for items when the user approaches the vehicle, re-scanning when the user exits, and generating alerts after a predetermined time period if items are still present. This periodic action approach allows the neural network to process images only when necessary rather than continuously, reducing energy consumption while maintaining detection accuracy
Solution Approach 2:
The system performs preliminary scanning actions before the user actually enters or exits the vehicle. By detecting items in advance during approach and exit phases, the system can make early determinations about forgotten items without requiring continuous high-energy monitoring throughout the entire vehicle operation cycle
Data Source
AI summary
Systems, methods and apparatuses to detect an item left in a vehicle and to generate an alert about the item. For example, a camera configured in a vehicle can be used to monitor an item associated with a user of the vehicle. The item as in an image from the camera can be identified and recognized using an artificial neural network. In response to a determination that the item recognized in the image is left in the vehicle after the user has exited the vehicle, an alert is generated to indicate that an item is in the vehicle but the user is leaving the vehicle.