Multi-sensor Security System with Adaptive Deterrence
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Solution Overview
Problem
Current security systems struggle to accurately and efficiently detect entities within their field of view and take appropriate actions in real-time, often leading to incorrect or delayed responses due to limitations in processing image data and low confidence in interpretation.
Innovation Solution
The system employs a combination of sensors such as cameras, radar, and microphones, using machine learning models to detect entities and determine their presence within specific zones, adjusting response times based on entity characteristics and behaviors, and executing deterrence actions such as alerting users or authorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single camera is used to monitor premises, then the device complexity is low, but the measurement precision and reliability of entity detection are insufficient
Solution Approach 1:
The patent combines multiple sensor types (camera, radar, microphone) into a unified sensor system that collaboratively detects entities. The camera provides visual data, radar provides motion and distance data, and microphone provides audio data, creating a multi-modal detection system that achieves high measurement precision while distributing the detection function across complementary sensors rather than relying on a single complex device
Solution Approach 2:
The sensor system is designed with multi-functionality where each sensor type serves multiple purposes: the camera detects entity presence and visual characteristics, the radar detects motion and distance, and the microphone detects sounds. This universal approach allows the system to achieve high detection accuracy through multiple functional perspectives without requiring each individual sensor to be overly complex
2Reliability
If camera footage is captured but not viewed by user in real-time, then the loss of time for response is minimized, but the reliability of preventing harmful activities remains low due to low confidence in interpretation
Solution Approach 1:
The system implements feedback loops where sensor data is continuously processed and analyzed. Machine learning models process the multi-modal sensor data and provide confidence scores for entity interpretation. When confidence is low, the system can request additional data or escalate to user review, creating a feedback mechanism that improves reliability without requiring constant real-time user monitoring of all footage
Solution Approach 2:
The patent introduces machine learning models as intermediaries between the raw sensor data and the security response decision-making process. These models process and interpret the complex multi-modal data, providing structured information and confidence metrics that bridge the gap between raw sensor inputs and reliable security responses, reducing the need for direct user interpretation of raw footage
3Measurement precision
If multiple sensors are used to detect entities, then the measurement precision improves, but the device complexity and processing requirements increase
Solution Approach 1:
The patent segments the detection function across multiple specialized sensors rather than using a single multi-functional device. Each sensor (camera, radar, microphone) is optimized for its specific detection modality, and the system processes each sensor type's data through dedicated processing pipelines before integration, reducing the complexity of integrating a single complex multi-functional device
Data Source
AI summary
Presented herein are system and methods for monitoring a premises. The system and methods can include one or more sensor devices, including one or more image capture devices and one or more processors. The systems and methods can detect, using the one or more sensor devices, the presence of an entity within a first zone of the environment. The systems and methods can determine that the entity corresponds to one or more criteria and determine a first threshold duration based at least in part on the one or more criteria that correspond to the entity. The systems and methods can further determine a duration the entity remains in the first zone after detection by the one or more sensor devices and execute a deterrence action based on the duration and the first threshold duration.