Method of incident scene focus area determination

Patent metadata

Field	Value
Publication number	US-9818042-B2
Application number	US-201514964277-A
Country	US
Kind code	B2
Filing date	Dec 9, 2015
Priority date	Dec 9, 2015
Publication date	Nov 14, 2017
Grant date	Nov 14, 2017

Abstract

Data analytics engines and methods of incident scene focus area determination. The method includes receiving a plurality of directional inputs from a plurality of sources. The method also includes assigning weighting factors to the plurality of directional inputs. The method further includes generating weighted position vectors for each of the plurality of sources based on the plurality of directional inputs and the weighting factors. The method also includes determining when the weighted position vectors for at least two sources of the plurality of sources intersect. The method further includes determining an intersection location and a confidence level based on the weighted position vectors of the at least two sources. The method also includes identifying an incident scene focus area based on the intersection location and the confidence level.

First claim

We claim:

1. A method of incident scene focus area determination, the method comprising:
receiving a plurality of directional inputs from a plurality of sources;
assigning weighting factors to the plurality of directional inputs;
generating weighted position vectors for each of the plurality of sources based on the plurality of directional inputs and the weighting factors;
determining when the weighted position vectors for at least two sources of the plurality of sources intersect;
determining an intersection location between the weighted position vectors of the at least two sources;
determining a confidence level for the intersection location based on the weighted position vectors of the at least two sources; and
identifying an incident scene focus area based on the intersection location and the confidence level.

2. The method of claim 1 , wherein the plurality of directional inputs includes at least one selected from a group consisting of a weapon fired direction, an eye direction, a weapon drawn direction, a head direction, a hand direction, and a camera direction, and a movement direction.

3. The method of claim 1 , wherein the plurality of directional inputs includes at least an eye direction and a movement direction, and wherein a first weighting factor assigned to the eye direction is greater than a second weighting factor assigned to the movement direction.

4. The method of claim 1 , wherein a size of the incident scene focus area is proportional to the confidence level.

5. The method of claim 1 , further comprising:
determining when a height off the ground of the weighted position vectors of the at least two sources is greater than a threshold; and
adding the incident scene focus area to a map when the height off the ground of the weighted position vectors of the at least two sources is greater than the threshold.

6. The method of claim 1 , further comprising:
detecting a face in image data captured by a camera of one of the at least two sources; and
adding the incident scene focus area to a map when the face is detected in the image data.

7. The method of claim 1 , further comprising:
adding the incident scene focus area to a map when the confidence level is greater than a first threshold; and
removing the incident scene focus area from the map when the confidence level is less than a second threshold,
wherein the second threshold is less than the first threshold.

8. The method of claim 1 , further comprising:
detecting a change in at least one selected from a first group consisting of the intersection location and the confidence level; and
updating, based on the change, at least one selected from a second group consisting of a location of the incident scene focus area and a size of the incident scene focus area.

9. The method of claim 1 , further comprising:
providing feedback to the at least two sources indicating that the weighted position vectors are being used to determine the incident scene focus area.

10. The method of claim 1 , further comprising:
detecting a split incident based on the weighted position vectors of the at least two sources; and
adding a split incident scene focus area to a map when the split incident is detected.

11. A data analytics engine for incident scene focus area determination, the data analytics engine comprising:
a transceiver configured to communicate with a plurality of sources through at least one communication network; and
an electronic processor electrically coupled to the transceiver and configured to:
receive a plurality of directional inputs from the plurality of sources,
assign weighting factors to the plurality of directional inputs,
generate weighted position vectors for each of the plurality of sources based on the plurality of directional inputs and the weighting factors,
determine when the weighted position vectors for at least two sources of the plurality of sources intersect,
determine an intersection location between the weighted position vectors of the at least two sources,
determine a confidence level for the intersection location based on the weighted position vectors of the at least two sources, and
identify an incident scene focus area based on the intersection location and the confidence level.

12. The data analytics engine of claim 11 , wherein the plurality of directional inputs includes at least one selected from a group consisting of a weapon fired direction, an eye direction, a weapon drawn direction, a head direction, a hand direction, and a camera direction, and a movement direction.

13. The data analytics engine of claim 11 , wherein the plurality of directional inputs includes at least an eye direction and a movement direction, and wherein a first weighting factor assigned to the eye direction is greater than a second weighting factor assigned to the movement direction.

14. The data analytics engine of claim 11 , wherein a size of the incident scene focus area is proportional to the confidence level.

15. The data analytics engine of claim 11 , wherein the electronic processor is further configured to:
determine when a height off the ground of the weighted position vectors of the at least two sources is greater than a threshold, and
add the incident scene focus area to a map when the height off the ground of the weighted position vectors of the at least two sources is greater than the threshold.

16. The data analytics engine of claim 11 , wherein the electronic processor is further configured to:
detect a face in image data captured by a camera of one of the at least two sources, and
add the incident scene focus area to a map when the face is detected in the image data.

17. The data analytics engine of claim 11 , wherein the electronic processor is further configured to:
add the incident scene focus area to a map when the confidence level is greater than a first threshold, and
remove the incident scene focus area to the map when the confidence level is less than a second threshold,
wherein the second threshold is less than the first threshold.

18. The data analytics engine of claim 11 , wherein the electronic processor is further configured to:
detect a change in at least one selected from a first group consisting of the intersection location and the confidence level, and
update, based on the change, at least one selected from a second group consisting on a location of the incident scene focus area and a size of the incident scene focus area.

19. The data analytics engine of claim 11 , wherein the electronic processor is further configured to:
provide feedback to the at least two sources indicating that the weighted position vectors are being used to determine the incident scene focus area.

20. The data analytics engine of claim 11 , wherein the electronic processor is further configured to:
detect a split incident based on the weighted position vectors of the at least two sources, and
add a split incident scene focus area to a map when the split incident is detected.

Assignees

• Motorola Solutions Inc

Inventors

• Capota Mircea
• Alazraki Scott M

Classifications

• G06V20/52 Primary

  Surveillance or monitoring of activities, e.g. for recognising suspicious objects (recognising microscopic objects G06V20/69) · CPC title

• G06F18/2113

  by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation · CPC title

• G06V10/25

  Determination of region of interest [ROI] or a volume of interest [VOI] · CPC title

• G06K9/50 Primary

  Physics · mapped topic

• G06K9/00255

  Physics · mapped topic