34results about How to "Improve gesture recognition rate" patented technology

The invention discloses a control method and an electronic device. The control method comprises the steps of determining a first operation region where an operation body conducts a gesture operation according to induction information obtained by at least two induction units, wherein induction information of different space regions can be obtained on the basis of the induction units, and the first operation region at least comprises at least one of different space regions; generating and executing a first control instruction corresponding to the first operation region at least on the basis of the configuration relationship of the operation region and the instruction, wherein the first control instruction is different from a second control instruction generated when the operation body conducts the gesture operation in a second operation region.

The present invention discloses a control method and an electronic device. The control method includes: determining a first operation area where an operating body performs gesture operation based on the sensing information acquired by the at least two sensing units, wherein, based on the at least The two sensing units can acquire sensing information of different spatial regions, the first operating region includes at least one of the different spatial regions; at least based on the configuration relationship between the operating region and the instruction, generate and execute the A first control instruction corresponding to the first operation area, wherein the first control instruction is different from a second control instruction generated when the operating body performs a gesture operation in the second operation area.

The invention discloses an isotropic three-dimensional gesture recognition method based on feature selection. The existing 3D gesture recognition algorithm does not consider the contribution of the extracted gesture-related features to the classification, and the redundant features affect the recognition rate. The present invention extracts 24 features from the collected three-dimensional coordinate data of gestures and inputs them into the random forest model, arranges the importance scores of each feature obtained by the training model from large to small, and selects among the 24 features arranged in k groups of each gesture The first n features of each group are combined into a combination feature. Based on the ten-fold cross-validation method and the Gaussian Naive Bayesian recognition model, the recognition rate of the Gaussian Naive Bayesian recognition model under 24 groups of combined features is obtained; The recognition rate of the naive Bayesian recognition model determines the selection of the combined features composed of the first few features for the final recognition model. The invention not only reduces the amount of feature-related data collection, simplifies the model calculation, but also improves the recognition rate.

The embodiment of the invention discloses a black screen gesture recognition method, device, storage medium and mobile terminal. The method includes monitoring the switching state of each black screen gesture in the application layer, and updating the black screen gesture switch in the driver layer according to the switching state; matching the turned on black screen gesture with the black screen gesture in the preset confusing gesture group, Judging according to the matching result whether to change the recognition condition of the opened black screen gesture; if so, then adopting the preset second recognition condition to replace the first recognition condition of the opened black screen gesture in the driver layer, and according to the second recognition The condition recognizes the gesture type of the blank screen gesture entered by the user. The above technical solution responds to the enabled black screen gestures according to the user's configuration of the black screen gestures, and makes targeted adjustments to the recognition conditions of the enabled black screen gestures, which can effectively avoid the occurrence of the low recognition rate of the current black screen gestures. Improved gesture recognition rate.

The embodiment of the invention discloses a gesture recognition method, device and electronic device, which relate to the technical field of data processing and can solve the problem of low gesture recognition efficiency in the prior art. The gesture recognition method in the embodiment of the present invention includes: obtaining a panoramic image including gesture operations captured by a single camera from a panoramic component; using a distortion correction algorithm corresponding to the panoramic component to perform a distortion correction operation on the panoramic image to obtain the correction performing a block operation on the corrected image to obtain a block image; extracting a gesture feature value of the block image, and determining a gesture operation command based on the gesture feature value. In addition, the embodiment of the invention also discloses a gesture recognition device and electronic equipment. Through the solutions of the embodiments of the present invention, the efficiency of gesture recognition can be effectively improved.

The invention provides a gesture control device and a gesture recognition method. The gesture control device comprises a computation terminal and a plurality of sensors, wherein the computation terminal is used for carrying out offline natural gesture modeling and online gesture recognition; the sensors are respectively arranged at the small arm, big arm and trunk of a tested object so as to correspondingly acquire the gesture coordinates of the small arm, big arm and trunk; and the sensors are connected with the computation terminal in a communication manner. In the online gesture recognition process, the computation terminal carries out computation processing on the basis of data acquired by the sensors, and the actual state, obtained through the computation processing, of the tested object is automatically compared with a gesture model obtained in the offline natural gesture modeling process of the computation terminal to complete the gesture recognition of the tested object. In the gesture control device, the computation terminal can process the repeated actions, so that the offline natural gesture modeling process and the online gesture recognition process are simplified, the gesture recognition rate is improved, and the control of natural gestures close to the human habits becomes possible.