Ultra-Range Gesture Recognition using a Web-Camera in Human-Robot Interaction (2311.15361v2)

Abstract: Hand gestures play a significant role in human interactions where non-verbal intentions, thoughts and commands are conveyed. In Human-Robot Interaction (HRI), hand gestures offer a similar and efficient medium for conveying clear and rapid directives to a robotic agent. However, state-of-the-art vision-based methods for gesture recognition have been shown to be effective only up to a user-camera distance of seven meters. Such a short distance range limits practical HRI with, for example, service robots, search and rescue robots and drones. In this work, we address the Ultra-Range Gesture Recognition (URGR) problem by aiming for a recognition distance of up to 25 meters and in the context of HRI. We propose the URGR framework, a novel deep-learning, using solely a simple RGB camera. Gesture inference is based on a single image. First, a novel super-resolution model termed High-Quality Network (HQ-Net) uses a set of self-attention and convolutional layers to enhance the low-resolution image of the user. Then, we propose a novel URGR classifier termed Graph Vision Transformer (GViT) which takes the enhanced image as input. GViT combines the benefits of a Graph Convolutional Network (GCN) and a modified Vision Transformer (ViT). Evaluation of the proposed framework over diverse test data yields a high recognition rate of 98.1%. The framework has also exhibited superior performance compared to human recognition in ultra-range distances. With the framework, we analyze and demonstrate the performance of an autonomous quadruped robot directed by human gestures in complex ultra-range indoor and outdoor environments, acquiring 96% recognition rate on average.

• The paper introduces a novel framework that extends gesture recognition to 25m using HQ-Net and GViT, achieving a 98.1% success rate.
• The study leverages a high-quality super-resolution model and hybrid graph-vision transformer to enhance low-resolution images effectively.
• Extensive experiments demonstrate robust performance across diverse environments, outperforming current state-of-the-art gesture recognition methods.

Ultra-Range Gesture Recognition Using a Web-Camera in Human-Robot Interaction

The article "Ultra-Range Gesture Recognition using a Web-Camera in Human-Robot Interaction" presents a detailed paper addressing the Ultra-Range Gesture Recognition (URGR) problem, aiming to enhance the distance at which gesture recognition is viable in Human-Robot Interaction (HRI) scenarios. The paper proposes a novel framework capable of recognizing gestures up to 25 meters using a simple RGB camera, which is significantly beyond the typical 4-7 meters range achieved by current state-of-the-art methods.

Main Contributions

1. HQ-Net Super-Resolution Model: A new Super Resolution (SR) model called High-Quality Network (HQ-Net) was developed to enhance the quality of low-resolution images resulting from the large distance between the user and the camera. The HQ-Net integrates several advanced image processing techniques such as convolutional layers, self-attention mechanisms, and the Canny edge detection algorithm to render detailed user images. This approach outperforms existing SR methods, achieving a Peak Signal-to-Noise Ratio (PSNR) of 34.45 dB, substantially higher than other models.
2. Graph-Vision Transformer (GViT):
   • Graph Convolutional Networks (GCNs): The GViT incorporates GCNs which effectively capture spatial dependencies by treating the image as a graph where each pixel is a node.
   • Vision Transformers (ViT): The integration of Vision Transformers enables the model to leverage the self-attention mechanism to capture global features and dependencies within the image.
   • Hybrid Architecture: By combining GCN and ViT, GViT effectively processes low-resolution images and recognizes gestures with a superior success rate of 98.1% over distances up to 25 meters.
3. Data Collection and Image Preprocessing: A comprehensive dataset H was collected, consisting of 347,483 labeled images from various distances and environments. The images were pre-processed to focus on users using YOLOv3, followed by quality enhancement through HQ-Net, ensuring high-quality input for the GViT model.

Experimental Validation and Results

The GViT model was evaluated against several existing gesture recognition models and demonstrated substantial improvements:

• The proposed framework achieved a success rate of 98.1% in gesture recognition in ultra-range distances, significantly outperforming notable gesture recognition systems such as SAM-SLR, MediaPipe Gesture Recognizer, and OpenPose, which showed poor performance beyond 7 meters.
• The human recognition benchmark test revealed a deterioration in human accuracy at distances beyond 19 meters, with an overall success rate of 78.4%, which further highlights the superiority of the URGR framework.
• GViT's performance remained robust across diverse environments and conditions, including outdoor, indoor, and courtyard settings, and specific edge cases like partial occlusions, poor lighting, and multiple participants.

Implications and Future Scope

The implications of this research are substantial for HRI, particularly in applications requiring remote gesture directives such as search and rescue operations, service robots, and interactive smart environments. The URGR framework's ability to extend gesture recognition to 25 meters and beyond can enable more practical and seamless interactions with robots.

Future Research Directions:

1. Temporal Inference for Dynamic Gestures: Extending the framework to include temporal information could enhance recognition performance, particularly for dynamic gestures.
2. Generalization to Longer Distances and Various Contexts: While the current framework handles up to 25 meters effectively, extending recognition capability to 40 meters, especially in challenging environments (e.g., adverse weather conditions), would be beneficial.
3. Integration with Additional Sensors: Combining data from other sensors such as depth cameras or LiDARs could provide a more holistic understanding and potentially improve robustness.
4. Application to Other Object Recognition Tasks: Adapting the framework for tasks such as surveillance, sports analytics, and medical diagnostics could open new avenues for AI-powered vision systems.
5. Enhancing Real-Time Performance: Addressing the latency issues and ensuring the framework runs onboard robots will be critical for practical deployment in dynamic environments.

In conclusion, the paper presents a comprehensive framework for ultra-range gesture recognition, pushing the boundaries of current capabilities in HRI and setting a strong foundation for future advancements in robot vision systems and interaction paradigms.