Edge Camera Human Presence Detection via Key Frame Neural Inference
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Solution Overview
Problem
Existing camera systems struggle to perform human recognition efficiently in edge devices due to high processing power requirements, limiting real-time human detection capabilities in low-power environments.
Innovation Solution
Implementing a method that combines multiple image analysis results within a camera using an optimized runtime environment to execute neural network-based inferencing models, allowing for real-time human recognition by selecting key frames and applying them to neural networks for likelihood determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural network-based inferencing models are executed in edge devices for human recognition, then human detection accuracy is improved, but processing power requirements increase
Solution Approach 1:
The patent segments the human recognition process into multiple independent neural network models that can be executed separately on edge devices. Each model handles specific aspects of human detection, allowing the system to achieve high accuracy while distributing computational load across multiple smaller, more efficient processing units rather than requiring one large power-intensive model
Solution Approach 2:
The patent transforms the neural network models into optimized parameter formats suitable for edge device execution. By converting models into specialized parameter representations and using quantization techniques, the system maintains detection accuracy while significantly reducing the processing power and memory requirements needed to execute the inferencing models on resource-constrained devices
2Measurement precision
If full-frame analysis is performed on multiple frames, then human recognition accuracy is improved, but processing time and computational cycles increase
Solution Approach 1:
The patent extracts and analyzes only the most informative frames from the video stream for full-frame neural network processing. By identifying and selecting key frames that contain the most relevant information for human detection, the system achieves high recognition accuracy while minimizing the number of computationally expensive full-frame analyses required, thereby reducing overall processing time
Solution Approach 2:
The patent applies partial analysis to selected frames rather than performing exhaustive full-frame analysis on every frame. By using a hybrid approach that combines selective full-frame analysis with faster partial-frame processing, the system achieves near-maximum accuracy with significantly reduced computational cycles and processing time compared to analyzing every frame in full detail
3Measurement precision
If human recognition is performed manually by operators, then detection accuracy is improved, but operational efficiency decreases
Solution Approach 1:
The patent implements automated neural network-based human recognition that operates independently without requiring manual operator intervention. The system performs self-service detection by automatically analyzing video frames, identifying humans, and generating detection results, thereby eliminating the need for human operators while maintaining high detection accuracy and significantly improving operational efficiency
Data Source
AI summary
A system and method for detecting human presence in or absence from a field-of-view of a camera by analyzing camera data using a processor inside of or adjacent to the camera itself. In an example, the camera can be integrated with or embedded in another edge-based sensor device. In an example, a video signal processing system receives image data from one or more image sensors and uses a local processing circuit to process the image data and determine if a human being is or is not present during a particular time, interval, or sequence of frames. In an example, the human being identification technique can be used in security or surveillance applications such as for home, business, or other monitoring cameras.