Vehicle Occupancy Detection via Camera and Neural Network
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Solution Overview
Problem
Current transportation systems lack efficient means to measure vehicle occupancy, particularly for ride-sharing and carpooling, as existing methods invade passenger privacy or are limited to front seat sensors, and there is a need for solutions that can be retrofitted for older vehicles to track occupants across the entire vehicle.
Innovation Solution
A device comprising a camera and processor using convolutional neural networks to estimate the number of occupants within a vehicle, capable of capturing images with various types of cameras (infrared, optical, night vision) and altering vehicle behavior based on occupancy data, while ensuring passenger privacy by not saving identifiable images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If seat sensors are installed to detect passenger occupancy, then occupancy detection capability is improved, but the sensors are typically only available in the front seats and cannot detect occupants in the entire vehicle
Solution Approach 1:
The camera system is designed to serve multiple functions: it captures images for occupancy detection, can detect occupants in all seats (not just front seats), and the same hardware can be used for both front and rear seat monitoring, making the system universally applicable throughout the entire vehicle interior
Solution Approach 2:
The vehicle interior is divided into multiple zones (front seats, rear seats, different rows) and the camera system captures the entire interior space, allowing occupancy detection to be segmented and applied to each zone independently while using a single comprehensive imaging system
2Measurement precision
If automated occupancy detection methods are implemented, then measurement accuracy is improved, but passenger privacy is invaded
Solution Approach 1:
The system extracts only the necessary information (occupancy count) from the captured images and discards the actual image data. The convolutional neural network processes images to determine occupant presence and quantity, then the images are deleted, retaining only the numerical occupancy result which does not reveal passenger identity or sensitive information
Solution Approach 2:
The convolutional neural network acts as an intermediary that processes the images and transforms them into occupancy counts without revealing identifiable information. The CNN performs the analysis and outputs only numerical data about occupant presence, serving as a mediator between the image capture and the final occupancy measurement
3Loss of information
If occupancy data collection is implemented for ride-sharing and carpooling, then transportation utilization measurement is improved, but existing systems lack the means to do so beyond manual counting
Solution Approach 1:
The system replaces manual counting methods (mechanical/human operation) with an automated computer vision system using cameras and convolutional neural networks. This substitution eliminates the need for drivers or passengers to manually report occupancy, automatically capturing and processing images to determine occupant numbers
Solution Approach 2:
The occupancy detection system is self-operating and requires no human intervention for data collection. The camera automatically captures images, the neural network automatically processes them to determine occupancy, and the system automatically stores the results, making the entire occupancy measurement process autonomous
Data Source
AI summary
Described herein are systems and methods for detecting the number of occupants in a vehicle. The detecting may be performed using a camera and a processing device. The detecting may be anonymous and the image of the interior of the vehicle is not stored on the processing device.