Video Surveillance Correlating Moving Objects and RF Signals
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Solution Overview
Problem
Current video surveillance systems lack real-time event detection capabilities, requiring manual monitoring of extensive video data to identify suspicious activities, and fail to specify interesting events for timely notifications.
Innovation Solution
The system indexes video surveillance databases with MAC addresses from WIFI-enabled devices and other wireless technologies, enabling queries for specific individuals and detecting events through RF signals to trigger data storage or alarms, integrating advanced query types into low-cost surveillance systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video surveillance systems record hundreds of hours of moving images onto video tape for manual monitoring, then complete coverage of all scenes is achieved, but the time and resources required to monitor and analyze the data increase significantly
Solution Approach 1:
The system extracts and isolates only the meaningful portions of video data by using motion detection to identify changing regions, then focuses analysis only on those specific areas rather than processing entire video frames. This extraction of relevant information from the vast video dataset reduces the time required to monitor surveillance footage while maintaining reliable detection of suspicious activities.
Solution Approach 2:
The patent replaces manual mechanical monitoring of video tapes with automated electronic image processing and analysis systems. The computer-based image processing section automatically detects motion, identifies suspicious patterns, and flags events for review, substituting human operators with automated algorithms that can process video data much faster and without fatigue.
2Extent of automation
If smart cameras provide intelligent data about objects and events, then real-time event detection capability is improved, but the complexity of software algorithms required increases
Solution Approach 1:
The system segments the complex task of smart surveillance into distinct modular components: motion detection module, region-of-interest identification module, event pattern recognition module, and data storage module. Each module performs a specific function with relatively simple algorithms, and they work together in sequence to achieve real-time event detection. This segmentation reduces the complexity of individual software components while maintaining the overall intelligence of the system.
3Productivity
If video motion detection triggers data storage or alarms, then automated response to detected events is achieved, but the ability to specify exactly what events are of interest is limited
Solution Approach 1:
The system implements dynamic event detection criteria that can be adjusted and configured based on specific surveillance needs. The definition of what constitutes a suspicious event is not fixed but can be dynamically modified through software configuration, allowing the system to adapt to different security requirements, locations, and time periods while maintaining automated real-time response capabilities.
Data Source
AI summary
A surveillance method periodically detects an image of the area, identifies and tracks each moving object in a succession of the detected images, detects radio frequency emissions from the area and correlates an identified object with a detected radio frequency emission. The method detects events in the tracking of the moving object. The method stores corresponding data optionally including image data in non-volatile memory upon detection of a combination of an event and a corresponding radio frequency emission. The method triggers an alarm such as an audible alarm, a visual alarm, an email, a short text message or a telephone call detection of a combination of an event and a corresponding radio frequency emission.