Gesture Recognition is an active field of research with applications such as automatic recognition of sign language, interaction of humans and robots or for new ways of controlling video games.
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Our in-lab study shows that GesturePod achieves 92% gesture recognition accuracy and can help perform common smartphone tasks faster.
Ranked #1 on Gesture Recognition on GesturePod
Multivariate time series (MTS) arise when multiple interconnected sensors record data over time.
On this basis, a new variant of LSTM is derived, in which the convolutional structures are only embedded into the input-to-state transition of LSTM.
Acquiring spatio-temporal states of an action is the most crucial step for action classification.
Ranked #1 on Hand Gesture Recognition on Jester val
SLR seeks to recognize a sequence of continuous signs but neglects the underlying rich grammatical and linguistic structures of sign language that differ from spoken language.
In contrast, we work on recognizing both gestures and longer, higher-level activites, or maneuvers, and we model the mapping from kinematics to gestures/maneuvers with recurrent neural networks.
Ranked #1 on Surgical Skills Evaluation on MISTIC-SIL
Gesture recognition is a hot topic in computer vision and pattern recognition, which plays a vitally important role in natural human-computer interface.
Ranked #1 on Hand Gesture Recognition on Cambridge
Consequently, this paper proposes applying transfer learning on aggregated data from multiple users, while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets.
We propose a Dynamic Graph-Based Spatial-Temporal Attention (DG-STA) method for hand gesture recognition.
Ranked #1 on Hand Gesture Recognition on DHG-14