Surveillance Pattern Learning for Unpredictable Event Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional surveillance systems rely solely on user-defined references to detect abnormal situations, failing to recognize events that users cannot perceive or predict, leading to inadequate performance in unforeseen circumstances.
Innovation Solution
A system and method that analyze time-based patterns in image and sound data from surveillance zones to learn and generate event models, detecting events by comparing real-time data with established patterns, and assigning priorities based on deviations from these models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surveillance systems use only user-defined references to detect abnormal situations, then the system operates simply and follows user requests, but it fails to detect abnormal situations that users cannot perceive or have not predicted
Solution Approach 1:
The system performs preliminary learning of normal patterns from historical surveillance data before actual event detection. The pattern learner analyzes image and sound data to establish baseline patterns of normal activities, which are then used as references for detecting abnormalities. This preliminary action enables the system to detect unforeseen abnormal situations without requiring users to pre-define all possible abnormal patterns.
Solution Approach 2:
The surveillance system automatically learns and adapts to the surveillance zone characteristics without requiring continuous user input. The pattern learner autonomously analyzes data and generates event models, while the event detector independently compares real-time data against these models. This self-service capability allows the system to improve its detection accuracy over time without increasing operational complexity for users.
2Reliability
If the system analyzes time-based patterns of image and sound data to learn and generate event models, then the system can detect unforeseen abnormal situations, but the processing complexity and computational requirements increase
Solution Approach 1:
The system divides the complex task of anomaly detection into separate functional modules: a pattern learner that analyzes historical data to establish normal patterns, and an event detector that compares real-time data against these patterns. This segmentation allows each module to specialize in specific processing tasks, improving overall detection accuracy while making the system architecture more manageable and maintainable.
Solution Approach 2:
The pattern learner performs preliminary analysis of historical surveillance data to generate event models representing normal patterns before actual event detection begins. By pre-processing and establishing baseline patterns in advance, the system reduces the computational burden during real-time detection, as the event detector only needs to compare current data against pre-established models rather than analyzing raw data from scratch.
3Loss of information
If the system compares real-time data with learned patterns and assigns priorities based on deviations, then the system provides comprehensive surveillance analysis, but the processing time and computational resources increase
Solution Approach 1:
The event detector focuses on detecting partial deviations from normal patterns rather than analyzing every aspect of the data in full detail. By identifying and prioritizing only the most significant deviations that indicate potential abnormal situations, the system provides comprehensive surveillance analysis while reducing processing time. Not all data points require equal scrutiny - only those showing meaningful deviations from established patterns.
Solution Approach 2:
The system implements priority assignment based on the degree of deviation from learned patterns, creating a feedback mechanism that directs attention to the most critical anomalies. Events are ranked by their deviation magnitude, allowing the system to process and report the most significant abnormalities first. This feedback-driven prioritization ensures information completeness for critical events while reducing processing time for less significant variations.
Data Source
AI summary
A system and method for providing surveillance data are provided. The system includes: a pattern learner configured to learn a time-based data pattern by analyzing at least one of image data of one or more images and sound data of sound obtained from a surveillance zone at a predetermined time or time period, and to generate an event model based on the time-based data pattern; and an event detector configured to detect at least one event by comparing the event model with a time-based data pattern of at least one of first image data of one or more first images and first sound data of first sound obtained from the surveillance zone.