AI Video Monitoring for Ski Lift Boarding Hazard Detection
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Solution Overview
Problem
Ski lift on-boarding and off-boarding locations pose safety risks due to the concentration of skiers and mechanical hazards, with human attendants often being unaware of developing problems or out of position to respond quickly.
Innovation Solution
An always-on, always-alert system using video cameras, a video processing module, and an AI engine to detect potential problems in real-time, initiating actions such as slowing or stopping the lift to mitigate risks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human attendants monitor on-boarding and off-boarding locations, then safety awareness can be provided, but the attendants are often unaware of developing problems or out of position to respond quickly
Solution Approach 1:
The patent replaces the mechanical human observation system with an automated video camera and AI image processing system. The camera continuously captures footage of on-boarding and off-boarding areas, while the AI engine automatically analyzes the video streams to detect potential safety issues, eliminating the limitations of human attendants' awareness and positioning.
Solution Approach 2:
The system enables self-monitoring and self-response capabilities through the AI engine that automatically detects problems and triggers appropriate responses without human intervention. The system serves itself by autonomously analyzing video data, identifying safety concerns, and initiating mitigation actions, making the safety monitoring independent of human attendants.
2Productivity
If an automated AI-based detection system is implemented, then real-time problem identification and quicker response can be achieved, but the system complexity increases with multiple components including cameras, video processing modules, and AI engines
Solution Approach 1:
The AI engine serves multiple functions within the system: it processes video data from multiple cameras, detects various types of safety issues (falls, objects on tracks, improper loading), and can trigger different response actions. This multi-functionality consolidates what would otherwise require separate specialized systems into a single versatile component.
Solution Approach 2:
The video processing module acts as an intermediary between the raw video cameras and the AI engine, preprocessing the video data to extract relevant frames and features. This intermediate processing step simplifies the workload of the AI engine and improves overall system efficiency by reducing the complexity at any single point.
3Reliability
If continuous video monitoring is performed to ensure always-on safety surveillance, then safety coverage is improved, but the energy consumption and processing requirements increase
Solution Approach 1:
Instead of continuously processing all video data at full capacity, the system uses periodic analysis where the AI engine examines video frames at strategically determined intervals and only when changes are detected. This approach maintains comprehensive safety coverage while significantly reducing average energy consumption compared to continuous full-power processing.
Solution Approach 2:
The system applies partial processing by focusing computational resources only on relevant portions of the video stream - such as areas where motion is detected or where potential safety issues are suspected. This selective processing maintains high safety monitoring effectiveness while reducing overall energy consumption by avoiding exhaustive analysis of every frame throughout every area.
Data Source
AI summary
Systems and methods for improved operations of ski lifts increase skier safety at on-boarding and off-boarding locations by providing an always-on, always-alert system that “watches” these locations, identifies developing problem situations, and initiates mitigation actions. One or more video cameras feed live video to a video processing module. The video processing module feeds resulting sequences of images to an artificial intelligence (AI) engine. The AI engine makes an inference regarding existence of a potential problem situation based on the sequence of images. This inference is fed to an inference processing module, which determines if the inference processing module should send an alert or interact with the lift motor controller to slow or stop the lift.