GRLib: An Open-Source Hand Gesture Detection and Recognition Python Library
원문 초록
Hand gesture recognition systems provide a natural way for humans to interact with computer systems. Although various algorithms have been designed for this task, a host of external conditions, such as poor lighting or distance from the camera, make it difficult to create an algorithm that performs well across a range of environments. In this work, we present GRLib: an open-source Python library able to detect and classify static and dynamic hand gestures. Moreover, the library can be trained on existing data for improved classification robustness. The proposed solution utilizes a feed from an RGB camera. The retrieved frames are then subjected to data augmentation and passed on to MediaPipe Hands to perform hand landmark detection. The landmarks are then classified into their respective gesture class. The library supports dynamic hand gestures through trajectories and keyframe extraction. It was found that the library outperforms another publicly available HGR system - MediaPipe Solutions, on three diverse, real-world datasets. The library is available at https://github.com/mikhail-vlasenko/grlib and can be installed with pip.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Visual Hand Gesture Recognition with Deep Learning: A Comprehensive Review of Methods, Datasets, Challenges and Future Research Directions
Diver-Robot Communication Dataset for Underwater Hand Gesture Recognition
Time Frequency Analysis of EMG Signal for Gesture Recognition using Fine grained Features
Fusing Posture and Position Representations for Point Cloud-Based Hand Gesture Recognition
ProFi-Net: Prototype-based Feature Attention with Curriculum Augmentation for WiFi-based Gesture Recognition
Improving Rating Prediction in Multi-Criteria Recommender Systems via a Collective Factor Model
토론
이 논문과 연결된 포럼 글이 아직 없습니다.