Multi-Area Motion Detection Surveillance System
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Solution Overview
Problem
Conventional surveillance systems face issues with erroneous motion detection due to external factors like weather changes and unrelated movements, leading to unnecessary recording and warning messages, resulting in inefficient storage usage and incorrect alerts.
Innovation Solution
A multi-area motion-detection surveillance system and method that allows users to define specific areas and set sensitivity thresholds for each, enabling targeted recording and alerting while ignoring irrelevant movements and environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion detection is applied to the entire surveillance video, then any motion is detected and recorded, but this causes erroneous judgments due to irrelevant movements like weather changes and unrelated objects
Solution Approach 1:
The surveillance video is divided into multiple predefined areas (e.g., first area, second area, third area) based on importance. Motion detection is then applied selectively to each area with different sensitivity levels, allowing the system to focus on critical zones while ignoring irrelevant movements in non-critical areas.
Solution Approach 2:
Different sensitivity thresholds are assigned to different areas of the surveillance video. High-sensitivity areas capture even minor movements, while low-sensitivity areas filter out minor disturbances. This local differentiation resolves the contradiction by making detection accuracy area-specific rather than uniform across the entire video.
2Measurement precision
If high sensitivity motion detection is used, then all movements are detected, but this leads to false alarms from irrelevant sources like cars on the road or pedestrians on the street
Solution Approach 1:
The surveillance area is segmented into multiple zones with different detection requirements. Critical areas use high sensitivity for precise detection, while non-critical areas use lower sensitivity to filter out false alarms from irrelevant movements like passing vehicles or pedestrians.
Solution Approach 2:
Each area is assigned a specific sensitivity level appropriate to its importance. This local quality differentiation allows high measurement precision in critical zones without suffering from the high false alarm rate that would result from applying the same high sensitivity across the entire surveillance area.
3Reliability
If continuous surveillance video recording is performed, then all events are captured, but this consumes huge storage capacity
Solution Approach 1:
The surveillance system segments the monitoring area into multiple zones and applies motion detection selectively to each. Recording is triggered only when motion is detected in predefined areas, eliminating the need for continuous recording of the entire video feed and thus reducing storage requirements while maintaining event capture reliability.
Solution Approach 2:
The system performs preliminary motion detection analysis before deciding to record. By pre-defining areas of interest and setting sensitivity thresholds, the system can determine in advance whether recording is necessary, avoiding storage of unnecessary video data while ensuring reliable capture of significant events.
4Reliability
If motion detection is applied to the entire video area, then no motion is missed, but this causes erroneous judgments when external factors like weather or sunshine variations affect the video
Solution Approach 1:
The surveillance area is divided into multiple segments, allowing the system to exclude or reduce sensitivity in areas prone to environmental interference (like windows showing weather changes) while maintaining high detection coverage in protected indoor areas. This segmentation preserves detection coverage without sacrificing precision in vulnerable zones.
Solution Approach 2:
Different areas are assigned different detection qualities based on their susceptibility to environmental factors. Areas affected by weather or sunshine variations are given lower sensitivity or excluded from high-priority monitoring, while stable areas maintain high detection accuracy. This local differentiation resolves the contradiction between comprehensive coverage and precise measurement.
Data Source
AI summary
A surveillance system having a multi-area motion-detection function is described. The surveillance system includes a display, an area selection device, and a threshold input device. The display shows a surveillance video. The area selection device selects a first area and a second area on the display screen. The threshold input device sets a motion-detection threshold of the first area and a motion-detection threshold of the second area. When the result of the motion-detection exceeds the motion-detection threshold of the first area, the surveillance video is stored in a storage medium. When the result of the motion-detection exceeds the motion-detection threshold of the second area, the surveillance video is stored in the storage medium.