Markered Region Gesture Pre-Processing Pipeline
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Solution Overview
Problem
Current gesture recognition systems in computing environments face challenges with high power consumption due to continuous image processing, even when no relevant imagery is being analyzed, leading to CPU utilization maxing out and limiting other applications.
Innovation Solution
A stepped and distributed pipeline process for gesture pre-processing that reduces power consumption by selectively performing CPU-intensive tasks, with initial threshold detection and user presence processing near the camera, and eliminating image processing outside a marked region, using a Threshold Detection, Target Presence, Segmentation, Hand Parser, and Gesture Recognition stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous image processing is performed for gesture recognition, then gesture recognition capability is maintained, but platform power consumption increases and CPU utilization maxes out
Solution Approach 1:
The image processing pipeline is segmented into multiple stages: threshold detection, target presence detection, segmentation, hand parser, and gesture recognition. Each stage processes only necessary portions of the image data, with early stages filtering out irrelevant data before it reaches subsequent stages, thereby reducing overall power consumption while maintaining gesture recognition capability.
Solution Approach 2:
The system performs preliminary actions by conducting threshold detection and target presence detection before full gesture recognition processing. These preliminary stages identify whether the image contains relevant hand gestures, and only if they do, does the system proceed to more computationally intensive processing stages, thus avoiding unnecessary power consumption during idle periods.
2Measurement precision
If full image processing is performed, then gesture recognition accuracy is maintained, but CPU availability for other tasks is reduced
Solution Approach 1:
The processing pipeline divides full image analysis into segmented stages that progressively refine results. Early stages perform coarse threshold detection and target presence identification, while later stages perform finer segmentation and gesture recognition. This segmentation allows the system to achieve accurate gesture recognition without requiring all CPU resources to be continuously allocated to processing.
Solution Approach 2:
The system employs periodic action by continuously monitoring image data through low-power threshold detection stages while maintaining CPU availability for other tasks. When gestures are detected, the system periodically activates more intensive processing stages, allowing accurate gesture recognition at times without permanently sacrificing CPU availability.
3Loss of energy
If image data transfer and processing is minimized, then platform power consumption is reduced, but gesture detection capability may be compromised
Solution Approach 1:
The system segments the image processing task into stages where early stages perform minimal data transfer and processing (threshold detection, target presence) while later stages perform more intensive processing only when needed. This segmentation reduces overall energy loss while maintaining gesture detection capability by processing only necessary portions of data.
Solution Approach 2:
The system extracts and processes only the most critical information (hand gestures) from the full image data stream. By using threshold detection and target presence stages, the system extracts relevant gesture information early, minimizing the amount of data that needs to be transferred and processed further, thus reducing power consumption while maintaining detection capability.
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 suppressed. In some embodiments, the markered region is aligned with, a mouse pad or designated desk area or a user input device such as a keyboard. Pixels evaluated by the system can be limited to a subset of the markered region.