Operating Room Traffic Monitoring via De-identified Video Analytics
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Solution Overview
Problem
Current methods for monitoring human traffic in operating rooms are inefficient, often requiring manual detection and annotation of head counts, which is time-consuming, and struggle with obstructions like masks and caps, and lack real-time feedback and privacy protection for individuals.
Innovation Solution
A computer-implemented system that processes video data from cameras to detect and track heads, hands, and bodies using object detection and recognition techniques, generating de-identified video data by blurring or obfuscating detected regions, and providing real-time head count data and traffic monitoring insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual detection and annotation methods are used for head count monitoring, then privacy protection is maintained, but monitoring efficiency is low and real-time feedback is not achieved
Solution Approach 1:
The patent replaces manual mechanical detection and annotation processes with automated computer vision systems using machine learning models. The system automatically detects, tracks, and counts heads in real-time video feeds, eliminating the need for manual frame-by-frame analysis and providing instantaneous monitoring results.
Solution Approach 2:
The system performs self-service by automatically processing video data, detecting heads, tracking movements, and generating counts without human intervention. The automated pipeline includes video processing, object detection, tracking, and result generation all executed by the system itself, enabling real-time monitoring efficiency.
2Speed
If automated object detection is used to track heads and bodies, then real-time monitoring is achieved, but individual privacy is compromised
Solution Approach 1:
The patent extracts only the necessary monitoring information (head count, traffic patterns, adverse event detection) from video data while deliberately excluding identifiable personal information. The system processes video feeds to generate aggregate statistics and alerts without capturing or storing images that could identify individuals, thus achieving real-time monitoring while protecting privacy.
3Measurement precision
If detectors are trained to detect body parts like heads and hands, then detection precision improves, but device complexity increases
Solution Approach 1:
The patent employs a universal object detection framework that can detect multiple body parts (heads, hands, bodies) and objects using a single trained model architecture. The system uses multi-class classification capabilities of detection models to identify various targets simultaneously, reducing the need for separate specialized detectors for each body part while maintaining high precision.
4Measurement precision
If video data is processed to track movement of multiple objects, then monitoring accuracy improves, but computational energy consumption increases
Solution Approach 1:
The patent segments the video processing task into distinct stages: frame extraction, object detection, tracking, and analysis. By processing video data in discrete frames and using efficient tracking algorithms that leverage temporal consistency, the system reduces redundant computations while maintaining accurate movement tracking of multiple objects throughout the video sequence.
Data Source
AI summary
Systems and methods for traffic monitoring in an operating room are disclosed herein. Video data of an operating room is received, the video data captured by a camera having a field of view for viewing movement of a plurality of individuals in the operating room during a medical procedure. An event data model is stored, the model including data defining a plurality of possible events within the operating room is stored. The video data is processed to track movement of objects within the operating room, the objects including at least one body part, and the processing using at least one detector trained to detect a given type of the objects. A likely occurrence of one of the possible events is determined based on the tracked movement.