Camera Hardware Acceleration for Local Person and Fall Detection
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Solution Overview
Problem
Existing camera systems have limited artificial intelligence capabilities for monitoring and require network connections to backend servers for processing, which can be technically challenging due to hardware limitations and inefficient software for local execution of machine learning models.
Innovation Solution
A camera system with a hardware accelerator that executes machine learning models locally, applying conditional logic to determine when to apply models like person detection, fall detection, and other safety models, enhancing monitoring capabilities without relying on network connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If camera systems use network connections to backend servers for machine learning processing, then monitoring capabilities are improved, but system reliability deteriorates due to dependency on network connectivity
Solution Approach 1:
The patent extracts the machine learning processing capability from the remote backend server and relocates it to a hardware accelerator integrated within the camera system itself. This extraction of computational dependency resolves the contradiction by maintaining advanced monitoring capabilities while eliminating network connectivity requirements for local processing operations.
Solution Approach 2:
The patent introduces a hardware accelerator as an intermediary component between the camera sensor and the processing system. This intermediary device enables local machine learning inference without requiring continuous network communication with remote servers, thereby improving system reliability while preserving monitoring functionality.
2Reliability
If camera systems execute machine learning models locally without network connections, then system reliability is improved, but device complexity increases due to hardware limitations
Solution Approach 1:
The patent extracts the machine learning model execution workload from the main camera processor and relocates it to a dedicated hardware accelerator. This separation of concerns reduces the complexity burden on the main processing unit while enabling reliable local execution of safety-critical models.
Solution Approach 2:
The patent replaces software-based machine learning execution with hardware-accelerated processing. By substituting the mechanical/software processing approach with dedicated hardware circuits optimized for neural network inference, the system achieves reliable local execution without excessive complexity in the main processor.
3Device complexity
If camera systems use inefficient software for local execution of machine learning models, then device complexity is reduced, but productivity deteriorates due to processing efficiency
Solution Approach 1:
The patent replaces inefficient software-based machine learning execution with dedicated hardware accelerator circuits. This substitution of software processing with hardware implementation dramatically improves processing efficiency for safety monitoring tasks while maintaining acceptable device complexity through modular hardware design.
Data Source
AI summary
Systems and methods are provided for machine learning based monitoring. Image data from a camera is received. On the hardware accelerator, a person detection model based on the image data is invoked. The person detection model outputs first classification result. Based on the first classification result, a person is detected. Second image data is received from the camera. In response to detecting the person, a fall detection model is invoked on the hardware accelerator based on the second image data. The fall detection model outputs a second classification result. A potential fall based on the second classification result is detected. An alert is provided in response to detecting the potential fall.