Gesture Pre-Processing Pipeline Reduces Power
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Solution Overview
Problem
Current gesture recognition systems in computing devices face challenges with high power consumption due to continuous video processing, especially when no relevant imagery is being analyzed, leading to CPU utilization maxing out and excessive power usage even during idle states.
Innovation Solution
A stepped and distributed pipeline process for gesture pre-processing that selectively performs CPU-intensive tasks, with initial threshold detection and user presence processing near the camera, and further processing stages distributed across the system to reduce power consumption, including a simplified skintone detection process and elimination of unnecessary image processing outside a marked region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous video processing is performed for gesture recognition, then gesture recognition capability is improved, but power consumption increases significantly
Solution Approach 1:
The video processing pipeline is segmented into multiple stages: initial threshold detection at the camera level, skintone detection stage, and CPU-based gesture recognition stage. This segmentation allows early filtering of non-relevant frames, preventing unnecessary processing and reducing overall power consumption while maintaining gesture recognition capability when needed.
Solution Approach 2:
Preliminary threshold detection and skintone analysis are performed before full gesture recognition processing. By pre-filtering video frames to identify only those containing potential hand gestures (through threshold detection and skintone color analysis), the system avoids unnecessary CPU processing and reduces power consumption during idle or non-gesture periods.
2Measurement precision
If full video stream processing is performed, then gesture detection accuracy is improved, but CPU utilization maxes out
Solution Approach 1:
The system extracts only the necessary processing tasks from full video stream analysis. By using threshold detection and skintone color analysis to identify relevant frames, the system extracts and processes only the essential information needed for gesture recognition, eliminating unnecessary CPU processing and reducing CPU utilization while maintaining detection accuracy.
Solution Approach 2:
The system performs partial processing by analyzing only specific regions (marker region) and applying simplified processing algorithms (threshold detection, skintone detection) before full gesture recognition. This partial action approach reduces CPU workload while maintaining sufficient accuracy for gesture identification.
3Reliability
If image processing is performed across the entire field of view, then gesture recognition completeness is improved, but processing time and power increase
Solution Approach 1:
The system applies local quality by focusing processing only on the marker region where hand gestures are expected to occur. By limiting threshold detection and skintone analysis to this specific region rather than the entire field of view, the system reduces processing time and power consumption while maintaining complete gesture recognition capability for relevant gestures.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly reduces power needed to transfer image data and processes, allowing for efficient hand-based navigational gesture processing while minimizing CPU cycles and thermal design power, thereby conserving platform power and improving gesture recognition efficiency.
Implementation Method 1
changes in a video stream may be detected by comparing current frame data to previous frame data
Implementation Method 2
skintone detection may be performed on a reduced set of pixels within the marker region
Data Source
AI summary
Techniques are disclosed for processing a video stream to reduce platform power by employing a stepped and distributed pipeline process, wherein CPU-intensive processing is selectively performed. The techniques are particularly well-suited for hand-based navigational gesture processing. In one example case, for instance, the techniques are implemented in a computer system wherein initial threshold detection (image disturbance) and optionally user presence (hand image) processing components are proximate to or within the system's camera, and the camera is located in or proximate to the system's primary display. In some cases, image processing and communication of pixel information between various processing stages which lies outside a markered region is eliminated. In some embodiments, the markered region is aligned with a mouse pad, desk area, or a user input device (e.g., keyboard). Pixels evaluated by the system can be limited to a subset of markered region. A simplified skintone detection process can be employed.