Gesture recognition method based on plum group and long-short-term memory network
A long-short-term memory and gesture recognition technology, applied in character and pattern recognition, neural learning methods, biological neural network models, etc., can solve the problems of recognition limitations, overcome the interference of environmental factors, overcome spatial complexity, and improve The effect of accuracy
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Embodiment 1
[0054] see Figure 1 to Figure 6 , a gesture recognition method based on Lie Group (Lie Group) and long-short-term memory network (LSTM), mainly comprises the following steps:
[0055] 1) Obtain the dynamic gesture skeleton video, and extract the hand skeleton image frame by frame. The device for obtaining dynamic gesture skeleton video is Intel depth camera RealSense.
[0056] 2) Preprocessing the hand skeleton image, the main steps are:
[0057] 2.1) Unify the number of hand bone images extracted from different dynamic gesture videos to ensure that the number of hand bone images in different dynamic gesture videos is consistent.
[0058] 2.2) Normalize the hand bone images to ensure that the hand bone sizes in all hand bone images are consistent.
[0059] 3) Extract the skeletal joint point data of the hand skeletal image, and put classification labels on it. Gestures are classified according to actions, mainly including left swing (the whole hand is waved to the left), ...
Embodiment 2
[0091] A kind of experiment that verifies the gesture recognition method based on Lie Group (Lie Group) and long-short-term memory network (LSTM), mainly comprises the following steps:
[0092] 1) Data acquisition, using Intel depth camera RealSense to extract hand bone joint point information, obtain gesture information, and preprocess the data;
[0093] Collect transactions through RealSense. The hand skeleton contains 21 joint points and 20 segments of bones, such as figure 2 shown. Five gestures were collected, including left swipe, right swipe, zoom in, zoom out, and open. Each gesture was repeated 20 times by 10 experimenters.
[0094] 2) Data preprocessing is to reduce the size of each data to ensure that the data size is consistent, and then normalize the data to ensure that the bone size in different samples is consistent. All data were normalized between 0 and 1 according to the following formula:
[0095]
[0096] in, Indicates the normalized data, x i Rep...
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