ML Video Analysis for Liquid Submersion Detection
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Solution Overview
Problem
Current CCTV systems face challenges in accurately detecting individuals who have fallen into liquid substances, leading to delayed assistance and unnecessary resource consumption due to the need for continuous monitoring and the requirement to detect a majority of body parts.
Innovation Solution
A method using a machine learning model trained to analyze video data from CCTV cameras, employing object recognition techniques like keypoint detection and pose estimation to classify individuals in liquid substances, with confidence factors modifying the detection accuracy and triggering assistance based on confidence thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous monitoring is performed to detect individuals in liquid substances, then detection reliability is improved, but resource consumption increases
Solution Approach 1:
The system performs periodic analysis of video data frames at configurable intervals rather than continuous monitoring. The machine learning model analyzes video frames periodically to detect individuals in liquid substances, allowing the system to balance detection reliability with resource consumption by adjusting the analysis frequency based on risk levels and operational requirements.
2Measurement precision
If detection of all body parts is required to confirm individual presence, then measurement precision is improved, but detection time increases
Solution Approach 1:
The system detects individuals by identifying partial body features rather than requiring complete detection of all body parts. The machine learning model is trained to recognize individuals in liquid substances based on visible portions of the body, allowing detection to proceed even when only part of the individual is visible above the liquid surface, thus reducing detection time while maintaining adequate precision.
Solution Approach 2:
The detection process is segmented into multiple stages: initial frame analysis for potential detections, followed by confidence threshold evaluation, and finally assisted detection using additional confidence factors. This segmentation allows the system to quickly identify potential individuals and then apply more rigorous verification only when necessary, balancing speed and accuracy.
3Reliability
If confidence threshold is set high to reduce false notifications, then reliability is improved, but response time decreases
Solution Approach 1:
Additional confidence factors serve as intermediaries to bridge the gap between initial detection confidence and final notification decisions. When an individual is detected with confidence near the threshold, these additional factors (such as behavioral analysis, contextual information, and temporal patterns) are evaluated to provide supplementary evidence, allowing the system to make more reliable notifications without excessively raising the base confidence threshold.
4Measurement precision
If machine learning model analyzes all video data continuously, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The machine learning model analyzes video data periodically rather than continuously processing every frame. The system configures analysis intervals based on operational needs, allowing the model to maintain high detection accuracy while significantly reducing computational load and processing time by analyzing a representative subset of video frames.
Solution Approach 2:
The system performs preliminary filtering of video data before applying the machine learning model. Video frames are pre-processed to identify those containing potential detections based on motion detection, scene changes, or other heuristic methods, allowing the computationally intensive machine learning analysis to be applied only to relevant frames, thus improving processing efficiency while maintaining detection accuracy.
Data Source
AI summary
A guest assistance platform may analyze video data of an environment that includes a liquid substance. The video data is analyzed using a machine learning model trained to detect objects in the liquid substance. The guest assistance platform may detect an individual in the liquid substance based on analyzing the video data and may determine a measure of confidence associated with detecting the individual in the liquid substance. The guest assistance platform may determine one or more confidence factors associated with detecting the individual in the liquid substance. The guest assistance platform may modify the measure of confidence using the one or more confidence factors and may determine whether the modified measure of confidence satisfies a confidence threshold. The guest assistance platform may selectively cause assistance to be provided to the individual or continue analyzing the video data based on whether the modified measure of confidence satisfies the confidence threshold.