Passenger Activity Recognition Using Object Detection in Cabins
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Solution Overview
Problem
Existing systems in aircraft cabins lack the ability to efficiently and unobtrusively monitor passenger activities and interactions with objects to anticipate needs and enhance crew service efficiency without disturbing passengers.
Innovation Solution
A person activity recognition system using an image sensor and processor to analyze image data with machine learning algorithms, such as CNN, to detect objects and determine passenger activities, integrated with a database for action correlation and privacy-protecting data handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image sensors and machine learning algorithms are used to monitor passenger activities, then passenger activity recognition accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex task of activity recognition into distinct components: image capture by sensors, image data transmission to the server, object detection by the processor, and activity determination by comparing detected objects with stored activity patterns. This segmentation allows each component to be optimized independently while managing overall system complexity.
Solution Approach 2:
The server acts as an intermediary between the image sensors in the aircraft cabin and the activity recognition database. The server receives image data, coordinates the detection and analysis processes, and manages data flow between components, thereby simplifying the architecture and reducing direct connectivity requirements.
2Loss of information
If image data is collected and stored for analysis, then activity detection capability is improved, but data storage requirements and privacy concerns increase
Solution Approach 1:
The system extracts only the essential information needed for activity recognition from the image data. Instead of storing complete images or extensive video sequences, the processor identifies and processes key visual features and objects, converting them into compact data representations that can be compared with the activity database, thereby minimizing storage requirements while maintaining detection capability.
Solution Approach 2:
The system processes image data in real-time and discards the original image data after extraction of relevant features. Only the extracted object information and activity classification results are retained, allowing the system to recover and utilize essential information while eliminating the need to store large volumes of raw image data.
3Loss of time
If real-time image analysis is performed onboard, then response time is improved, but computational power requirements and energy consumption increase
Solution Approach 1:
The system dynamically adjusts its processing approach based on available computational resources and energy constraints. The processor prioritizes analysis of critical objects and activities, adjusting the depth and intensity of image processing in real-time to maintain acceptable response times while managing energy consumption on the aircraft's power system.
Data Source
AI summary
A person activity recognition system (1) comprising: an image sensor (2) arranged to collect at least one image of a personal environment and generate image data (7) from the at least one image; and a processor (3) communicatively connected to the image sensor (2) to receive image data (7), and to a memory (4) and a database (5); the memory (4) including a list of detectable objects that may be present in the personal environment and corresponding results (8) relating to an activity of the person; and the processor (3) configured to execute a plurality of machine-readable instructions stored in a non-transitory computer readable medium in the memory (4), wherein the instructions, when executed by the processor (3), cause the processor (3) to: receive the image data (7); analyse the image data (7) to detect one or more objects in the personal environment; compare the detected object(s) with the list of detectable objects; and output to the database (5) a result (8) relating to an activity of the person.