Systems, methods, apparatuses, and devices for identifying, tracking, and managing unmanned aerial vehicles

• US 10,025,991 B2
• Filed: 11/08/2016
• Issued: 07/17/2018
• Est. Priority Date: 11/08/2016
• Status: Active Grant

• Current Assignee: Dedrone Holdings, Inc.
• Original Assignee: Dedrone Holdings, Inc.
• Inventors: Seeber, Rene, Seebach, Ingo, Meyer, Henning, Schoeler, Markus, Baumgart, Kai, Scheibe, Christian, Prantl, David
• Primary Examiner(s): Baker, Charlotte M
• Application Number:
  US15/346,298
• Publication Number:
  US 20180129882A1
• Time in Patent Office: 616 Days
• Field of Search: 382103, 725 41, 725 43
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/20104 Interactive definition of region of interest [ROI]
• CPC Class Codes: G06T 2207/30212 Military
• CPC Class Codes: G06T 2207/30232 Surveillance
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06T 7/136 involving thresholding
• CPC Class Codes: G06V 10/255 Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
• CPC Class Codes: G06V 20/13 Satellite images
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69

Abstract

Systems, methods, and apparatus for identifying and tracking UAVs including a plurality of sensors operatively connected over a network to a configuration of software and/or hardware. Generally, the plurality of sensors monitors a particular environment and transmits the sensor data to the configuration of software and/or hardware. The data from each individual sensor can be directed towards a process configured to best determine if a UAV is present or approaching the monitored environment. The system generally allows for a detected UAV to be tracked, which may allow for the system or a user of the system to predict how the UAV will continue to behave over time. The sensor information as well as the results generated from the systems and methods may be stored in one or more databases in order to improve the continued identifying and tracking of UAVs.

Claims

1. A method for identifying an aerial object type corresponding to an aerial object in a particular air space, comprising the steps of:

receiving a first video frame and a second video frame from a video feed of the particular air space, the first video frame comprising a first plurality of pixels and the second video frame comprising a second plurality of pixels, wherein the video feed is captured by a particular video sensor proximate to the particular air space and wherein the first video frame is captured earlier in time than the second video frame;

identifying a first region of interest (ROI) in the first video frame and a second ROI in the second video frame, the first ROI and the second ROI each comprising an image of an object flying within the particular air space, wherein the first ROI comprises a subset of the first plurality of pixels and the second ROI comprises a subset of the second plurality of pixels;

comparing a determined size of the first ROI to a determined size of the second ROI to determine a delta size parameter between the first ROI and the second ROI;

determining whether the delta size parameter is within a predetermined size threshold expected for a particular object type;

upon determination that the delta size parameter is within the predetermined size threshold, comparing a determined center position of the first ROI to a determined center position of the second ROI to determine a spatial location change of the object within the particular air space;

determining whether the spatial location change of the object is within a predetermined position change threshold expected for the particular object type; and

upon determination that the spatial location change of the object is within the predetermined position change threshold, denoting the object in the image as the particular object type.

2. The method of claim 1, wherein the particular object type comprises an unmanned aerial vehicle (UAV).

3. The method of claim 1, wherein the first video frame immediately precedes the second video frame in the video feed.

4. The method of claim 1, further comprising the step of storing the first video frame and/or the second video frame in a database.

5. The method of claim 4, further comprising the step of associating the first video frame and/or the second video frame in the database with an indication that the object in the image comprises a UAV.

6. The method of claim 1, wherein the first ROI and/or the second ROI comprises a rectangular shape.

7. The method of claim 1, further comprising the step of initiating an alert to a system user that the particular object type has been detected in the particular air space.

8. A system for identifying an aerial object type corresponding to an aerial object in a particular air space, comprising:

a video sensor proximate to the particular air space, wherein the video sensor is configured to collect and transmit a video feed, the video feed including a first video frame and a second video frame, the first video frame comprising a first plurality of pixels and the second video frame comprising a second plurality of pixels, wherein the first video frame is captured earlier in time than the second video frame; and

a processor operatively couple to the video sensor, the processor operative to;

identify a first region of interest (ROI) in the first video frame and a second ROI in the second video frame, the first ROI and the second ROI each comprising an image of an object flying within the particular air space, wherein the first ROI comprises a subset of the first plurality of pixels and the second ROI comprises a subset of the second plurality of pixels;

compare a determined size of the first ROI to a determined size of the second ROI to determine a delta size parameter between the first ROI and the second ROI;

determine whether the delta size parameter is within a predetermined size threshold expected for a particular object type;

upon determination that the delta size parameter is within the predetermined size threshold, compare a determined center position of the first ROI to a determined center position of the second ROI to determine a spatial location change of the object within the particular air space;

determine whether the spatial location change of the object is within a predetermined position change threshold expected for the particular object type; and

upon determination that the spatial location change of the object is within the predetermined position change threshold, denote the object in the image as the particular object type.

9. The system of claim 8, wherein the particular object type comprises an unmanned aerial vehicle (UAV).

10. The system of claim 8, wherein the first video frame immediately precedes the second video frame in the video feed.

11. The system of claim 8, wherein the system further comprises a database, and the processor is further operative to store the first video frame and/or the second video frame in a database.

12. The system of claim 11, wherein the processor is further operative to associate the first video frame and/or the second video frame in the database with an indication that the object in the image comprises a UAV.

13. The system of claim 8, wherein the first ROI and/or the second ROI comprises a rectangular shape.

14. The system of claim 8, wherein the processor is further operative to initiate an alert to a system user that the particular object type has been detected in the particular air space.