Camera Gesture Buffering Mechanism for Resource-Constrained Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Non-contact gesture detection systems in computing face challenges with high resource demands and inability to turn the gesturing feature on using a non-contact gesture, leading to degraded performance or constant power consumption.
Innovation Solution
Implementing a buffering mechanism that intermittently analyzes video data, sampling and analyzing frames to detect gestures, and processing buffered data only when gesture evidence is found, allowing for reduced resource usage and enabling gesture detection without continuous high-frame-rate streaming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video data are continuously streamed and analyzed for gesture detection, then gesture detection capability is maintained, but resource demands increase substantially
Solution Approach 1:
The system implements periodic sampling of video data at a sampling rate of at least once per video time interval, rather than continuous analysis. This periodic action reduces resource demands while maintaining gesture detection capability by processing data intermittently at strategically chosen moments.
Solution Approach 2:
The system buffers video data to a buffer with a buffer capacity that corresponds to a video time interval before analysis. This preliminary buffering action allows the system to accumulate data in advance, enabling efficient batch processing and reducing the need for continuous real-time analysis resources.
2Adaptability or versatility
If the gesturing feature is left in an on state to enable gesture detection, then gesture functionality is available, but performance degrades due to continuous resource consumption
Solution Approach 1:
The system dynamically adjusts its operation by sampling video data at variable intervals and processing buffered data only when gesture evidence is detected. This dynamic approach allows the gesturing feature to remain available while adapting resource consumption to actual usage patterns, preventing continuous performance degradation.
Solution Approach 2:
The system uses sampled video data to detect gesture evidence and automatically triggers processing of buffered video data only when needed. This self-service mechanism allows the system to manage its own resource allocation based on detected gesture patterns, maintaining functionality while optimizing performance.
3Use of energy by moving object
If video data are sampled at a low rate to reduce resource usage, then resource demands decrease, but gesture detection accuracy may be compromised
Solution Approach 1:
The system performs preliminary buffering of video data at the native video frame rate before sampling. This preliminary action ensures that even though sampling occurs at a lower rate, the complete video data is preserved in the buffer, allowing accurate gesture detection when buffered data is processed in response to gesture evidence.
Solution Approach 2:
The buffer acts as an intermediary between the low-rate sampling process and the full-resolution video data. It stores complete video frames at high fidelity while sampling occurs at reduced rates, allowing the system to maintain detection accuracy by processing high-quality buffered data when gestures are detected rather than relying solely on low-resolution sampled frames.
Data Source
AI summary
A method can include buffering video data to a buffer that includes a buffer capacity that corresponds to a video time interval; sampling video data at a sampling rate of at least once per video time interval; processing the sampled video data for gesture evidence; and, responsive to gesture evidence in the sampled video data, processing the buffered video data for additional gesture evidence. Various other apparatuses, systems, methods, etc., are also disclosed.