Lightweight CNN Hand Detection for Resource-Constrained FPV Devices
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Solution Overview
Problem
Existing hand detection systems in first-person view (FPV) for augmented and virtual reality applications face challenges in achieving real-time performance on resource-constrained devices without compromising accuracy, particularly in environments with varying lighting conditions and complex backgrounds, and lack robust, lightweight modules for hand keypoint detection and gesture interpretation.
Innovation Solution
A lightweight convolutional neural network (CNN) system that localizes and classifies hands using a novel bounding box loss function, enabling high-precision hand detection without requiring additional depth or IR sensors, and is optimized for resource-constrained environments such as smartphones and head-mounted devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If state-of-the-art deep learning models (YOLOv2, MobileNetv2, Faster R-CNN) are used for hand detection, then detection accuracy is improved, but computational cost increases and real-time performance is not achieved in resource-constrained environments
Solution Approach 1:
The patent segments the hand detection task into two distinct stages: (1) hand candidate region detection using a simplified detector, and (2) hand keypoint localization using a lightweight regression model. This segmentation allows each component to be optimized independently, achieving real-time performance on mobile devices while maintaining high accuracy through specialized processing at each stage.
Solution Approach 2:
The patent implements dynamic adaptive processing by adjusting the level of processing based on detection confidence. High-confidence detections undergo minimal processing, while low-confidence detections receive more intensive analysis. This dynamic approach optimizes computational resources in real-time, balancing speed and accuracy according to actual detection needs.
2Productivity
If rule-based approaches (texture features, color histograms, HOG classifiers) are used for hand detection, then computational cost is reduced, but robustness deteriorates in unusual backgrounds or varying lighting conditions
Solution Approach 1:
The patent performs preliminary action by pre-training deep convolutional neural network features on large-scale image datasets before deployment. These pre-learned features capture robust visual patterns that generalize well across varying lighting conditions and backgrounds. During real-time detection, these pre-computed features are applied without requiring complex rule-based processing, achieving both speed and robustness.
Solution Approach 2:
The patent changes parameters by transforming the detection problem from classification-based (rule-based approaches) to regression-based keypoint localization. This parameter transformation allows the system to directly predict hand keypoint coordinates and confidence scores, improving robustness to background variations while maintaining computational efficiency through optimized regression models.
3Measurement precision
If additional depth or IR sensors are added for hand detection, then detection accuracy in complex environments is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual depth map by copying and processing RGB image data through simulated depth estimation algorithms. Instead of requiring physical depth sensors, the system generates synthetic depth information from standard color cameras, achieving accurate hand detection in complex environments while maintaining device simplicity and cost-effectiveness.
Data Source
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AI summary
A system and a method for verification of a source code are provided. There as many techniques available that can be used for verification of software codes, however, it is difficult to determine appropriate technique that can be utilized for verification of a given software code. In an embodiment, the system receives a source code encoded with one or more specifications to be verified. A static analysis of the source code is performed to identify program features of the source code. The program features may include, but are not limited to, multiple return paths, loops with an unstructured control flow, loops with arrays, short ranges and numerical loops. Based on the identification of the program features, verification techniques are applied to the source code for the verification. Each verification technique of the one or more verification techniques is applied for a predetermined period of time and in a predefined order.