Hierarchical Crowd Behavior Detection System
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Solution Overview
Problem
Current video-based surveillance systems for crowd monitoring are inefficient in accurately detecting and interpreting crowd behavior due to environmental changes, limited foreground object detection, and lack of configurability, making them unreliable for dynamic and unpredictable crowd scenarios.
Innovation Solution
A computer-automated method that processes video and audio data, along with environmental sensors, to detect hierarchical human and crowd features, allowing for configurable behavior detection using behavior description language and hybrid human detectors, enabling accurate and efficient crowd behavior analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional video-based surveillance systems are used for crowd monitoring, then the system is low cost and portable, but the system is unreliable for accurately detecting crowd behavior due to environmental changes and limited detection capabilities
Solution Approach 1:
The patent combines multiple detection technologies (video surveillance, radar, audio sensors, environmental sensors) into an integrated crowd monitoring system. This multi-sensor fusion approach improves reliability by cross-validating detections across different modalities while managing complexity through centralized processing architecture.
Solution Approach 2:
The surveillance system is designed to perform multiple functions: crowd detection, behavior analysis, density estimation, and environmental monitoring. This multi-functional approach improves reliability across diverse scenarios without requiring separate specialized systems for each function.
2Measurement precision
If background modeling approach is used to detect crowds, then the system can estimate crowd locations and density, but the approach is less than reliable for modeling lighting, weather, and camera-related changes
Solution Approach 1:
The system employs dynamic background modeling that adapts to changing environmental conditions. The background model is continuously updated to account for lighting changes, weather variations, and camera movements, allowing the system to maintain detection precision in dynamic environments rather than using static models.
Solution Approach 2:
The patent introduces foreground object detection as an intermediary step between background modeling and crowd analysis. By detecting foreground objects (people) separately from background changes, the system can accurately identify crowds even when environmental conditions vary, overcoming the limitations of pure background modeling.
3Adaptability or versatility
If configurable behavior detection is implemented, then the system provides flexibility for detecting different crowd behaviors, but the system complexity increases due to multiple detection algorithms
Solution Approach 1:
The behavior detection system is segmented into modular components: basic behavior primitives (moving, stationary, gathering), intermediate behavior patterns, and complex crowd behaviors. This hierarchical segmentation allows configurable detection of different behavior levels while managing complexity through reusable modular detection algorithms.
Solution Approach 2:
The system implements configurable detection levels where users can activate only the behavior detection algorithms necessary for their specific application. Rather than implementing all possible detection algorithms simultaneously, the system allows selective activation of detection capabilities, reducing operational complexity while maintaining versatility.
Data Source
AI summary
The present invention is directed to a computer automated method of selectively identifying a user specified behavior of a crowd. The method comprises receiving video data but can also include audio data and sensor data. The video data contains images a crowd. The video data is processed to extract hierarchical human and crowd features. The detected crowd features are processed to detect a selectable crowd behavior. The selected crowd behavior detected is specified by a configurable behavior rule. Human detection is provided by a hybrid human detector algorithm which can include Adaboost or convolutional neural network. Crowd features are detected using textual analysis techniques. The configurable crowd behavior for detection can be defined by crowd behavioral language.