Video Assistance Platform for Advance Guest Needs Detection
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Solution Overview
Problem
Existing systems struggle to accurately and efficiently recognize guest characteristics, such as mobility restrictions, in real-time video feeds, leading to inadequate preparation for emergent events like evacuations.
Innovation Solution
A system utilizing machine learning models to analyze video data from camera devices, recognizing individuals and their characteristics, such as mobility aids, to generate assistance information that can be used to prepare for and respond to emergent events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are used to analyze video data to recognize guest characteristics, then the accuracy of identifying mobility restrictions is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary analysis of video data to identify guests with mobility restrictions before emergent events occur. By detecting and storing assistance information about guest characteristics in advance, the system prepares assistance plans proactively, reducing the need for complex real-time analysis during emergencies while maintaining high accuracy in identifying mobility needs.
2Loss of time
If real-time video analysis is performed to detect guest characteristics, then the response time to emergent events is improved, but the processing speed and computational resources required worsen
Solution Approach 1:
The system performs video analysis and identifies guest characteristics in advance of emergent events, storing assistance information about mobility restrictions and other needs. This preliminary action allows the system to retrieve pre-analyzed data during emergencies rather than performing complex real-time analysis, thus improving response time without requiring sustained high processing speeds.
Solution Approach 2:
The video analysis process is segmented into distinct phases: initial video data collection, characteristic recognition using machine learning models, assistance information generation, and emergency response activation. By dividing the processing into segments, the system can perform intensive analysis during low-demand periods and switch to lightweight retrieval operations during emergencies, maintaining both accuracy and processing efficiency.
3Reliability
If comprehensive guest characteristics are collected and stored, then the quality of assistance provided during emergent events is improved, but the data storage requirements and privacy concerns increase
Solution Approach 1:
The system extracts only the essential assistance information from video data, such as mobility restrictions and key characteristics relevant to emergency response. Rather than storing comprehensive video feeds or all guest details, the system isolates and stores only the critical data elements needed for providing effective assistance during emergent events, reducing storage requirements while maintaining reliability.
4Adaptability or versatility
If machine learning models are trained to recognize multiple guest characteristics, then the versatility of the assistance system is improved, but the training time and computational resources required worsen
Solution Approach 1:
The system employs a universal machine learning model architecture capable of recognizing multiple guest characteristics including mobility restrictions, age groups, and other relevant features. This multi-functional model is trained once to handle various characteristic types, reducing the need for separate training processes for each characteristic and improving versatility while managing training time and resources efficiently.
Data Source
AI summary
An assistance platform may analyze video data of an environment. Based on analyzing the video data, the assistance platform may recognize an individual out of a plurality of objects in the environment and may recognize a plurality of characteristics exhibited by the individual. The recognized characteristics may affect ability of the individual to respond to an event. The assistance platform may store assistance information identifying the plurality of characteristics; detect that an event has occurred in the environment; and retrieve the assistance information in response to detecting that the event has occurred. The assistance platform may determine, based on the assistance information, an action to be performed to assist the individual The assistance platform may cause the action to be performed to assist the individual; and delete the assistance information after causing the action to be performed.