Multi-sensor based user interface

Patent metadata

Field	Value
Publication number	US-10168785-B2
Application number	US-201615060525-A
Country	US
Kind code	B2
Filing date	Mar 3, 2016
Priority date	Mar 3, 2015
Publication date	Jan 1, 2019
Grant date	Jan 1, 2019

Abstract

An apparatus and method for gesture detection and recognition. The apparatus includes a processing element, a radar sensor, a depth sensor, and an optical sensor. The radar sensor, the depth sensor, and the optical sensor are coupled to the processing element, and the radar sensor, the depth sensor, and the optical sensor are configured for short range gesture detection and recognition. The processing element is further configured to detect and recognize a hand gesture based on data acquired with the radar sensor, the depth sensor, and the optical sensor.

First claim

What is claimed is:

1. An apparatus for gesture detection and recognition, the apparatus comprising:
a processing element;
a radar sensor;
a depth sensor; and
an optical sensor, wherein the radar sensor, the depth sensor, and the optical sensor are coupled to the processing element, and wherein the radar sensor, the depth sensor, and the optical sensor are configured for short range gesture detection and the processing element is configured to identify a type of hand gesture by combining data acquired with the radar sensor, data acquired with the depth sensor, and data acquired with the optical sensor, wherein the data acquired with the radar sensor is registered to the data acquired with the depth sensor,
wherein registering the data acquired with the radar sensor to the data acquired with the depth sensor comprises transforming three-dimension (3D) coordinates of the data acquired with the radar sensor to the depth sensor's coordinate frame, wherein said registering further comprises: observing 3D coordinates of a spherical volume concurrently with both the radar sensor and the depth sensor, determining a best-fit transformation function between the 3D coordinates of the spherical volume observed by both the radar sensor and the depth sensor, and using the transformation function to transform the 3D coordinates of the data acquired with the radar sensor to the depth sensor's coordinate frame.

2. The apparatus as described in claim 1 , wherein the radar sensor is in an always-on mode during a period in which the depth sensor and the optical sensor are turned off, wherein the depth sensor and the optical sensor are activated and a gesture recognition process to identify the type of hand gesture is performed only in response to the radar sensor detecting an amount of motion above a threshold amount that lasts for at least a threshold length of time.

3. The apparatus as described in claim 1 , wherein the radar sensor, the depth sensor, and the optical sensor are a portion of a user interface device of a vehicle.

4. The apparatus as described in claim 1 , wherein a portion of the processing element is configured to function as a deep neural network (DNN).

5. The apparatus as described in claim 4 , wherein the DNN comprises two 3D convolutional layers and two fully-connected layers.

6. The apparatus as described in claim 1 , wherein the radar sensor, the depth sensor, and the optical sensor are configured for gesture detection and recognition under low light conditions.

7. The apparatus as described in claim 1 , wherein the processing element is a graphics processing unit (GPU).

8. The apparatus as described in claim 1 , wherein the radar sensor, the depth sensor, and the optical sensor are configured for gesture detection and recognition within a range of one meter.

9. The apparatus as described in claim 1 , wherein the hand gesture is a dynamic hand gesture and wherein further the processing element is configured to automatically determine a command associated with the dynamic hand gesture.

10. A system for hand gesture detection, the system comprising:
a processor;
a first sensor comprising a radar;
a second sensor comprising a depth sensor; and
a third sensor comprising an optical sensor, wherein the first sensor, the second sensor, and third sensor are coupled to the processor, and wherein the first sensor, the second sensor, and the third sensor are configured for short range gesture detection and recognition and wherein further the processor is configured to identify a type of hand gesture by combining data acquired with the first sensor, data acquired with the second sensor, and data acquired with the third sensor, wherein the data acquired with the radar is registered to the data acquired with the depth sensor,
wherein registering the data acquired with the radar to the data acquired with the depth sensor comprises transforming three-dimension (3D) coordinates of the data acquired with the radar to the depth sensor's coordinate frame, wherein said registering further comprises: observing 3D coordinates of a spherical volume concurrently with both the radar and the depth sensor, determining a best-fit transformation function between the 3D coordinates of the spherical volume observed by both the radar and the depth sensor, and using the transformation function to transform the 3D coordinates of the data acquired with the radar to the depth sensor's coordinate frame.

11. The system as described in claim 10 , wherein the first sensor, the second sensor, and the third sensor are a portion of a user interface device for use in a vehicle.

12. The system as described in claim 10 , wherein a portion of the processor is configured to function as a deep neural network (DNN).

13. The system as described in claim 10 , wherein the processor is a graphics processing unit (GPU).

14. A mobile apparatus comprising:
a processing element;
a radar sensor;
a depth sensor; and
an optical sensor, wherein the radar sensor, the depth sensor, and the optical sensor are coupled to the processing element, and wherein the radar sensor, the depth sensor, and the optical sensor are configured for short range gesture detection and recognition and wherein further the processing element is configured to identify a type of hand gesture of a driver by combining data received from the radar sensor, data received from the depth sensor, and data received from the optical sensor, and wherein the processing element is configured to automatically determine the type of the hand gesture performed and a command associated with the hand gesture, wherein the data acquired with the radar sensor is registered to the data acquired with the depth sensor,
wherein registering the data acquired with the radar sensor to the data acquired with the depth sensor comprises transforming three-dimension (3D) coordinates of the data acquired with the radar sensor to the depth sensor's coordinate frame, wherein said registering further comprises: observing 3D coordinates of a spherical volume concurrently with both the radar sensor and the depth sensor, determining a best-fit transformation function between the 3D coordinates of the spherical volume observed by both the radar sensor and the depth sensor, and using the transformation function to transform the 3D coordinates of the data acquired with the radar sensor to the depth sensor's coordinate frame.

15. The mobile apparatus as described in claim 14 , wherein the processing element is configured to function as a neural network.

16. The mobile apparatus as described in claim 14 , wherein the processing element is a graphics processing unit (GPU).

17. The apparatus as described in claim 2 , wherein the threshold amount of motion is selected from the group consisting of: a threshold velocity of the motion; and a threshold distance of the motion.

Assignees

• Nvidia Corp

Inventors

• Molchanov Pavlo
• Gupta Shalini
• Kim Kihwan
• Pulli Kari

Classifications

• G06F18/25

  Fusion techniques · CPC title

• G06F18/24

  Classification techniques · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06N3/084

  Backpropagation, e.g. using gradient descent · CPC title