Video Surveillance System with Adaptive Model Weighting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional video surveillance systems rely solely on human observation, which limits the ability to efficiently process multiple objects and events simultaneously, leading to inefficient monitoring of monitored areas.
Innovation Solution
A video surveillance system and method that utilizes multiple video capture devices and sensing devices to capture and analyze video streams, determine target objects and events, and generate notification events through analysis models, enabling efficient and flexible monitoring management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human observation is used to monitor objects and events, then the system is simple to operate, but the processing efficiency of multiple objects and events simultaneously is low
Solution Approach 1:
The patent replaces the mechanical human observation system with an automated electronic surveillance system comprising video capture devices, sensing devices, and processing modules. The image recognition module automatically identifies target objects in video streams while sensing devices detect events, eliminating the need for human visual monitoring and enabling simultaneous processing of multiple objects and events.
Solution Approach 2:
The system performs self-monitoring and self-analysis through automated image recognition and event detection algorithms. The processing modules automatically analyze video streams and sensing data without requiring human intervention, allowing the system to serve itself in detecting and responding to multiple objects and events simultaneously.
2Reliability
If multiple video capture devices and sensing devices are deployed, then the monitoring coverage and detection capability are improved, but the system complexity and data processing burden increase
Solution Approach 1:
The patent divides the surveillance system into distinct functional modules: video capture devices for visual monitoring, sensing devices for event detection, image recognition modules for object identification, and processing modules for data analysis. Each module handles specific tasks independently, allowing multiple devices to be deployed without proportionally increasing overall system complexity.
Solution Approach 2:
The processing modules are designed to handle multiple types of data from various video capture devices and sensing devices through unified image recognition and event determination algorithms. This multi-functional capability allows the system to process diverse data streams simultaneously without requiring separate processing chains for each device type.
3Productivity
If automated image recognition and event determination are implemented, then the monitoring efficiency is enhanced, but the accuracy and adaptability of event recognition may be compromised
Solution Approach 1:
The system incorporates feedback mechanisms where processing modules continuously analyze video streams and sensing data, adjust recognition parameters based on detected patterns, and refine event determination algorithms. This feedback loop enables the system to maintain high monitoring efficiency while improving event recognition accuracy through adaptive learning from accumulated data.
Solution Approach 2:
The image recognition and event determination algorithms are designed to be dynamic and adaptable, adjusting their parameters and thresholds based on real-time conditions and detected patterns. This dynamic behavior allows the system to maintain high efficiency across varying scenarios while preserving accurate event recognition through adaptive parameter adjustment.
Data Source
AI summary
A video surveillance method and a video surveillance system applied the method are provided. The method includes capturing an image of at least a part of a monitored area to obtain a plurality of video streams; sensing the monitored area to obtain a plurality of sensing data; if an image of an object of a video stream is determined as a target object, determining whether the target object triggers a target event according to one of the sensing data corresponding to the video stream; if the target object is determined as triggering the target event, outputting a feature value corresponding to the target object according to a preset analysis condition, the video stream including the target object and the target event; and generating a notification event corresponding to the target object according to the feature value and a model weight value corresponding to the target object.