Hand Gesture Region of Interest Localization via Segmentation
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Solution Overview
Problem
Current methods for detecting regions of interest in head-mounted video devices are computationally expensive and require predefined shapes, making them unsuitable for wearable devices with limited processing resources, and fail to accurately determine user focus in real-time.
Innovation Solution
A method that uses a hand segmentation algorithm to detect pixels corresponding to hands in an ego-centric video, identifies a hand enclosure, and localizes a region of interest based on this enclosure, allowing any shape to be used for selection, with morphological operations to filter out false positives and negatives, and performs actions on the object within the selected region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If template matching with predefined shapes is used to detect hand gestures, then the method can identify specific gestures, but the computational cost increases significantly and power consumption rises
Solution Approach 1:
The hand gesture detection is segmented into two stages: first, a lightweight hand detection algorithm identifies hand regions; second, a separate region of interest detection algorithm processes only the hand-enclosed areas. This segmentation avoids the need for computationally expensive template matching across the entire image while maintaining detection accuracy.
Solution Approach 2:
The patent extracts only the necessary information (hand pixels and enclosed regions) from the image using segmentation algorithms, rather than processing the entire image with template matching. This extraction approach reduces computational load and power consumption while preserving detection precision for the specific task of region of interest identification.
2Measurement precision
If template matching with predefined shapes is used, then specific gestures can be recognized, but the method fails to detect unexpected hand shapes and regions of interest are missed
Solution Approach 1:
The system transitions from static template matching to dynamic region enclosure detection. Instead of requiring hands to match predefined shapes, the system dynamically identifies regions enclosed by hand pixels, allowing any shape or configuration to be detected as long as it encloses a region of interest. This dynamic approach significantly improves adaptability while maintaining detection accuracy.
3Measurement precision
If sliding-window-based template matching is used for hand gesture detection, then gesture recognition is achieved, but processing time increases and real-time performance deteriorates
Solution Approach 1:
The processing pipeline is segmented into efficient stages: hand detection using optimized algorithms, region enclosure identification, and ROI extraction. This segmentation eliminates the need for time-consuming sliding-window template matching while maintaining real-time performance through targeted processing of only relevant image regions.
Solution Approach 2:
Instead of performing exhaustive template matching across the entire image (excessive action), the system applies partial processing by first identifying hand regions and then only analyzing the enclosed areas. This partial action approach maintains sufficient detection accuracy while dramatically improving processing speed and real-time capability.
Data Source
AI summary
A method, non-transitory computer readable medium, and apparatus for localizing a region of interest using a hand gesture are disclosed. For example, the method acquires an image containing the hand gesture from the ego-centric video, detects pixels that correspond to one or more hands in the image using a hand segmentation algorithm, identifies a hand enclosure in the pixels that are detected within the image, localizes a region of interest based on the hand enclosure and performs an action based on the object in the region of interest.