One Shot Learning Framework for Crowd Behavior Recognition
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Solution Overview
Problem
Current methods for detecting behavior in crowds are inefficient in recognizing patterns prior to their occurrence, especially in environments like airports and sporting arenas, where improved security measures are needed to prevent crimes.
Innovation Solution
A one shot learning framework that uses video analytics to generate metadata, affect scores, and signatures from camera data to recognize behaviors, allowing for real-time matching and action when a query behavior is detected, leveraging pairwise matching and machine learning to classify behaviors based on a single observation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional behavior detection methods are used in crowds, then comprehensive behavior analysis can be achieved, but processing time increases and efficiency decreases
Solution Approach 1:
The patent segments crowd behavior analysis into individual person detection and tracking units. Each person is analyzed independently through camera feeds, extracting features like position, velocity, and behavior patterns separately, then aggregating results. This segmentation enables parallel processing of multiple individuals simultaneously, dramatically improving efficiency while reducing overall processing time.
Solution Approach 2:
The system performs preliminary action by pre-defining behavior patterns and criteria before real-time analysis. Behavior signatures, motion patterns, and anomaly thresholds are established in advance, allowing the system to quickly match observed behaviors against predefined templates during live monitoring, rather than analyzing each behavior from scratch.
2Measurement precision
If more camera data and video analytics streams are processed, then behavior detection accuracy improves, but data processing complexity increases
Solution Approach 1:
The patent extracts only the most relevant features from camera data and video analytics streams, such as position coordinates, velocity vectors, and key behavioral markers. By filtering out redundant information and focusing on critical parameters, the system maintains high detection accuracy while significantly reducing processing complexity and computational burden.
Solution Approach 2:
The system introduces intermediary processing layers that translate complex camera data into simplified behavioral representations. Metadata streams serve as intermediaries between raw video data and final behavior detection, organizing and preprocessing information before analysis, thereby reducing the complexity of subsequent processing stages.
3Reliability
If traditional learning methods are used for behavior recognition, then robust behavior classification can be achieved, but training data requirements increase
Solution Approach 1:
The patent applies partial action by using only essential behavioral features and key motion patterns for training the recognition system, rather than requiring comprehensive datasets covering all possible behaviors. The system focuses on learning the most discriminative features needed for reliable classification, achieving robust performance with reduced training data requirements.
Data Source
AI summary
Provided are techniques for assessing individual or crowd level behavior based on image data analysis. For example, in one embodiment, the techniques may include generating signatures representative of an observed behavior based on video data and performing pairwise matching by determining whether the first signature matches a second signature indicative of a query behavior.