arXiv:1705.05884
공식 코드 · Papers with Code
Static Gesture Recognition using Leap Motion
원문 초록
In this report, an automated bartender system was developed for making orders in a bar using hand gestures. The gesture recognition of the system was developed using Machine Learning techniques, where the model was trained to classify gestures using collected data. The final model used in the system reached an average accuracy of 95%. The system raised ethical concerns both in terms of user interaction and having such a system in a real world scenario, but it could initially work as a complement to a real bartender.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
SLRNet: A Real-Time LSTM-Based Sign Language Recognition System
Data-Free Class-Incremental Gesture Recognition with Prototype-Guided Pseudo Feature Replay
EgoEvGesture: Gesture Recognition Based on Egocentric Event Camera
Duo Streamers: A Streaming Gesture Recognition Framework
EgoHand: Ego-centric Hand Pose Estimation and Gesture Recognition with Head-mounted Millimeter-wave Radar and IMUs
DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology Modeling
토론
이 논문과 연결된 포럼 글이 아직 없습니다.