Abnormal Behavior Recognition via Skeleton Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting abnormal behavior in various environments, such as homes and workplaces, are limited in their ability to effectively monitor and classify unusual behavior in real-time across both indoor and outdoor settings, particularly for safety and health monitoring applications.
Innovation Solution
A system and method that captures video streams, extracts body skeleton data, and classifies it as normal or abnormal behavior using online and offline modules, generating alerts for abnormal behavior detection, and utilizes machine learning and template matching algorithms to build and update behavior databases for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video stream data is captured and analyzed in real-time to detect abnormal behavior, then safety monitoring capability is improved, but system complexity and computational resource requirements increase
Solution Approach 1:
The system segments the behavior recognition task into distinct modules: video capture module, skeleton extraction module, behavior classification module, and alert generation module. This segmentation allows each module to be optimized independently and processed in parallel, reducing overall system complexity while maintaining real-time monitoring capability.
Solution Approach 2:
The patent introduces body skeleton data as an intermediary representation between raw video streams and behavior classification. By converting video data into simplified skeleton representations, the system reduces computational complexity while preserving essential movement information needed for abnormal behavior detection.
2Measurement precision
If body skeleton data is extracted from video streams to classify behavior, then behavior recognition accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary extraction of body skeleton data from video streams before behavior classification occurs. This preliminary processing organizes and simplifies the data structure in advance, making the subsequent classification process more efficient and reducing overall processing time while maintaining high recognition accuracy.
3Adaptability or versatility
If the system is designed to work in both indoor and outdoor environments, then versatility is improved, but system complexity increases
Solution Approach 1:
The patent designs a universal behavior recognition system that functions across both indoor and outdoor environments using the same core methodology. The system processes video streams and extracts skeleton data using environment-agnostic algorithms, allowing it to adapt to different settings without requiring separate specialized systems for each environment.
Data Source
AI summary
A method for recognizing abnormal behavior is disclosed, the method includes: capturing at least one video stream of data on one or more subjects; extracting body skeleton data from the at least one video stream of data; classifying the extracted body skeleton data as normal behavior or abnormal behavior; and generating an alert, if the extracted skeleton data is classified as abnormal behavior.