Robert Boschgmbh

Robert Bosch Gmbh

Shubhang Bhatnagar

Sharath Gopal

Liu Ren

A computer-implemented system and method relate to gesture recognition. A machine learning model includes a first subnetwork, a second subnetwork, and a third subnetwork. The first subnetwork generates feature data based on sensor data, which includes a gesture. The feature data is divided into a set of patches. The second subnetwork selects a target patch of feature data from among the set of patches. The third subnetwork generates gesture data based on the target patch of feature data. The gesture data identifies the gesture of the sensor data. Command data is generated based on the gesture data. A device is controlled based on the command data.

System and method for long-distance recognition and personalization of gestures

A computer-implemented system and method relate to gesture recognition. A machine learning system is trained using a training dataset of sensor data that include a set of gestures. The training dataset includes at least a first subset that displays a first gesture. Loss data is generated based on a first loss function that includes a first cross entropy loss and a second cross entropy loss. Parameters of the machine learning system are updated based on the loss data. The machine learning system is outputted and configured for gesture recognition of the set of gestures. The machine learning system includes (i) a first subnetwork to generate feature data based on the sensor data, (ii) a second subnetwork to extract a selected patch of the feature data, and (iii) a third subnetwork to generate gesture data based on a classification of the corresponding feature data of the selected patch. The first cross entropy loss is based on a first performance of the second subnetwork in relation to the training dataset. The second cross entropy loss is based on a second performance of third subnetwork in relation to the training dataset.

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Shubhang Bhatnagar