Depth Camera Security Classification for False Alarm Reduction
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Solution Overview
Problem
Existing security systems struggle to differentiate between threatening and non-threatening motion, such as distinguishing between intruders and pets, leading to false alarms and inefficient resource allocation.
Innovation Solution
A computer-vision based security system using a depth camera and classification algorithms to isolate and classify moving objects as threats, harmless, or unknown, with the ability to update the classification algorithm based on human input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion detection is used to monitor the environment, then motion detection capability is provided, but false alarms occur due to inability to distinguish threatening from non-threatening movements
Solution Approach 1:
The system segments the classification process into multiple stages: initial automated classification using computer vision algorithms, followed by selective human review only for ambiguous cases. This segmentation allows the system to maintain high reliability while managing complexity by handling different types of detections differently.
Solution Approach 2:
The patent introduces an intermediary human reviewer who acts as a mediator between automated detection and final threat determination. This intermediary resolves ambiguous cases that automated systems cannot classify with sufficient confidence, improving overall reliability without requiring complete human oversight of all detections.
2Measurement precision
If all motion data is transmitted to remote processing service, then comprehensive analysis is possible, but data transmission volume and processing load increase
Solution Approach 1:
The system extracts and transmits only the essential classification results and ambiguous cases to the remote processing service, rather than transmitting all raw motion data. This extraction approach maintains measurement precision for threat detection while significantly reducing data transmission volume and associated energy consumption.
Solution Approach 2:
The system performs partial classification locally using automated algorithms, transmitting only the portions of data that require further remote analysis. This partial action approach balances local processing efficiency with remote analytical capability, reducing overall energy consumption while maintaining accuracy.
3Reliability
If human review is required for all classifications, then classification accuracy improves, but response time and operational efficiency decrease
Solution Approach 1:
The review process is segmented so that clear, unambiguous detections are handled automatically without human review, while only ambiguous or uncertain cases are forwarded for human evaluation. This segmentation maintains high productivity for straightforward cases while ensuring accuracy for difficult cases.
Solution Approach 2:
The automated classification system serves itself for clear-cut cases, making independent determination without human intervention. This self-service capability handles the majority of routine detections efficiently, reserving human resources only for cases that truly require expert judgment.
Data Source
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AI summary
A method for securing an environment. The method includes obtaining a two-dimensional (2D) representation of a three-dimensional (3D) environment. The 2D representation includes a 2D frame of pixels encoding depth values of the 3D environ - ment. The method further includes identifying a set of foreground pixels in the 2D representation, defining a foreground object based on the set of foreground pixels. The method also includes classifying the foreground object, and taking an action based on the classification of the foreground object.