In today’s rapidly developing fields of artificial intelligence and virtual reality, the importance of gesture recognition technology has attracted more and more attention. Gesture recognition technology can transform human body language into digital signals, so that computers can better understand human intentions. However, in order to train machine learning algorithms and deep learning models, a large number of gesture data sets are essential.
Gesture action data set is a set of data obtained by human performing actions and capturing action data through special equipment. These data usually contain joint angles, bone positions and motion sequences. Gesture action data set is a key resource for training and verifying gesture recognition algorithms and models.
In the fields of artificial intelligence and virtual reality, gesture recognition technology has been widely used in gesture control, gesture interaction, sign language translation and other fields. For example, in the field of virtual reality, gesture recognition technology can realize a natural and intuitive user interaction experience by tracking the user’s hand movements. In the field of human-computer interaction, gesture recognition technology can control computers and intelligent devices by analyzing gestures. In addition, gesture recognition technology is also widely used in health care fields, such as rehabilitation training and biomedical engineering.
Data Hall has developed several sets of high-quality training data sets for smart home scenes, which can be applied to tasks such as voice interaction, voice control, gesture control and abnormal behavior detection. It has been favored by many companies. The data includes 3D face recognition data, 200 people wake up the phone to collect voice data, 559,460 segments of 50 kinds of dynamic gesture recognition data, 8,643 pieces of 14 kinds of abnormal image video data, etc., which makes smart home products better understand the owner’s needs and man-machine communication more intelligent.
Now, more and more researchers and developers need to access large-scale gesture data sets to train their algorithms and models. Fortunately, many data sets have been published. For example, UCI machine learning library contains several gesture action data sets, including hand movement data set, action recognition data set and so on. In addition, Kinect dataset and challenger gesture challenge dataset are also important resources in the field of gesture recognition. These data sets not only help researchers and developers to research and develop gesture recognition algorithms, but also help enterprises and organizations to develop applications in virtual reality, game development and other fields.
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