gesturerecognition

Gesture recognition is the task of interpreting human gestures using sensors or cameras to produce machine-readable commands or interpretations. It covers hand gestures, body poses, and facial expressions, and can rely on modalities such as RGB cameras, depth sensors, inertial measurement units, or fused data. The technology supports human–computer interaction, sign language recognition, gaming, and robotics.

Early approaches used hand-crafted features like Haar-like features, optical flow, or skeleton tracking. Contemporary systems mainly rely on machine learning, particularly deep learning. Representations include raw video, depth maps, or skeleton sequences, and models combine spatial and temporal processing with CNNs, RNNs, or transformers. Some methods detect pose and classify gestures, while others learn end-to-end.

Prominent datasets include NTU RGB+D, Chalearn, EgoGesture, and SHREC. Evaluation typically reports accuracy or confusion matrices, with benchmarks emphasizing cross-subject or cross-view performance. Applications range from gesture-based interfaces and sign language recognition to virtual reality and assistive devices. Real-time performance and on-device inference are common goals.

Key challenges include appearance and viewpoint variability, occlusion, lighting, speed of gestures, and cultural differences. Privacy concerns and biases in collected data motivate ongoing work in privacy-preserving sensing, domain adaptation, and few-shot or zero-shot learning to improve generalization.