Smoke detection method and apparatus

• US 20070188336A1
• Filed: 02/13/2006
• Published: 08/16/2007
• Est. Priority Date: 02/13/2006
• Status: Active Grant

• Current Assignee: Axonx Fike Corporation
• Original Assignee: axonX LLC
• Inventors: Privalov, George
• Granted Patent: US 7,769,204 B2
• Time in Patent Office: Days
• US Class Current: 340/628
• CPC Class Codes: G06T 7/254 involving subtraction of images
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G08B 17/125 by using a video camera to detect fire or smoke

Abstract

An apparatus for detecting smoke in a monitored area includes: (a) a means for capturing a series of successive video images of the monitored area as a series of two-dimensional bitmaps having a specified number of pixels, (b) a means for examining this series of bitmaps to identify areas in successive bitmaps of adjacent pixels that have the potential for being used as indicators for the existence of smoke in the monitored area, (c) a means for storing these identified areas from the bitmaps, (d) a means for associating, on the basis of the likelihood of the existence of smoke in the monitored area, the identified areas from successive bitmaps so as to form a cluster of overlapping regions within those bitmaps, and (d) a means for analyzing the identified areas so as to identify the temporal changes in these areas which are indicative of the presence of smoke.

Claims

1. An apparatus for detecting smoke in a monitored area, said apparatus comprising:

a means for capturing a series of successive video images of said monitored area in the form of a series of two-dimensional bitmaps having a specified number of pixels,

a means for examining said series of bitmaps to identify areas in successive bitmaps of adjacent pixels that have the potential for being used as indicators for the existence of smoke in said monitored area,

wherein said resulting identified areas from said series of bitmap forming a cluster of identified areas,

a means for storing said cluster of identified areas from said bitmaps, and

a means for analyzing said cluster of identified areas so as to identify the temporal changes in said areas which are indicative of the presence of smoke in said series of successive video images.

2. The apparatus as recited in claim 1, further comprising:

a means for characterizing, prior to said analysis step, the data contained in each of said identified areas such that the amount of said data is reduced and specifiable information pertaining to said identified areas is preserved so as to allow one to analyze said reduced data to identify the presence of smoke in said monitored area, and

wherein said means for analyzing said identified areas is configured so as to operate on said reduced data.

3. The apparatus as recited in claim 1, further comprising:

a means for generating an alarm when said means for analyzing said identified areas identifies said temporal changes that are indicative of the presence of smoke.

4. The apparatus as recited in claim 2, further comprising:

a means for generating an alarm when said means for analyzing said identified areas identifies said temporal changes that are indicative of the presence of smoke.

5. The apparatus as recited in claim 1, wherein:

said means for examining said series of bitmaps, to identify areas in successive bitmaps of adjacent pixels that have the potential for being used as indicators for the existence of smoke in said monitored area, is configured so that said means accomplishes said identification by selecting those pixels having an approximate uniform change in intensity.

6. The apparatus as recited in claim 2, wherein:

said means for examining said series of bitmaps, to identify areas in successive bitmaps of adjacent pixels that have the potential for being used as indicators for the existence of smoke in said monitored area, is configured so that said means accomplishes said identification by selecting those pixels having an approximate uniform change in intensity.

7. The apparatus as recited in claim 1, wherein:

said means for analyzing said cluster of identified areas is configured so that said means accomplishes said analysis by selecting said temporal changes chosen from the group consisting of those temporal changes which indicate an ever-increasing number of pixels in those identified areas of said cluster or those temporal changes which indicate that the increase in the size of said identified areas occurs to a greater extent in the top rather than the bottom part of said identified areas, and

said top and bottom portion of said identified areas being defined with respect to the direction of influence of gravity within said monitored area.

8. The apparatus as recited in claim 2, wherein:

said means for analyzing said cluster of identified areas is configured so that said means accomplishes said analysis by selecting said temporal changes chosen from the group consisting of those temporal changes which indicate an ever-increasing number of pixels in those identified areas of said cluster or those temporal changes which indicate that the increase in the size of said identified areas occurs to a greater extent in the top rather than the bottom part of said identified areas, and

said top and bottom portion of said identified areas being defined with respect to the direction of influence of gravity within said monitored area.

9. The apparatus as recited in claim 5, wherein:

said means for analyzing said cluster of identified areas is configured so that said means accomplishes said analysis by selecting said temporal changes chosen from the group consisting of those temporal changes which indicate an ever-increasing number of pixels in those identified areas of said cluster or those temporal changes which indicate that the increase in the size of said identified areas occurs to a greater extent in the top rather than the bottom part of said identified areas, and

said top and bottom portion of said identified areas being defined with respect to the direction of influence of gravity within said monitored area.

10. The apparatus as recited in claim 6, wherein:

said means for analyzing said cluster of identified areas is configured so that said means accomplishes said analysis by selecting said temporal changes chosen from the group consisting of those temporal changes which indicate an ever-increasing number of pixels in those identified areas of said cluster or those temporal changes which indicate that the increase in the size of said identified areas occurs to a greater extent in the top rather than the bottom part of said identified areas, and

said top and bottom portion of said identified areas being defined with respect to the direction of influence of gravity within said monitored area.

11. The apparatus as recited in claim 2, wherein:

said means for analyzing said cluster of identified areas is configured so that said means accomplishes said analysis by making use of a neural network.

12. The apparatus as recited in claim 6, wherein:

said means for analyzing said cluster of identified areas is configured so that said means accomplishes said analysis by making use of a neural network.

13. A method for detecting smoke in a monitored area, said method comprising the steps of:

capturing a series of successive video images of said monitored area in the form of a series of two-dimensional bitmaps having a specified number of pixels,

examining said series of bitmaps to identify areas in successive bitmaps of adjacent pixels that have the potential for being used as indicators for the existence of smoke in said monitored area,

wherein said resulting identified areas from said series of bitmap forming a cluster of identified areas,

storing said cluster of identified areas from said bitmaps, and

analyzing said cluster of identified areas so as to identify the temporal changes in said areas which are indicative of the presence of smoke in said series of successive video images.

14. The method as recited in claim 13, further comprising the step of:

characterizing, prior to said analysis step, the data contained in each of said identified areas such that the amount of said data is reduced and specifiable information pertaining to said identified areas is preserved so as to allow one to analyze said reduced data to identify the presence of smoke in said monitored area, and

wherein said step of analyzing said identified areas is performed on said reduced data.

15. The method as recited in claim 13, wherein

said examining step involves accomplishing said identification by selecting those pixels having an approximate uniform change in intensity,

said analyzing step involves selecting temporal changes chosen from the group consisting of those temporal changes which indicate an ever-increasing number of pixels in those identified areas of said cluster or those temporal changes which indicate that the increase in the size of said identified areas occurs to a greater extent in the top rather than the bottom part of said identified areas, and

wherein top and bottom portion of said identified areas being defined with respect to the direction of influence of gravity within said monitored area.

16. The method as recited in claim 14, wherein

said examining step involves accomplishing said identification by selecting those pixels having an approximate uniform change in intensity,

said analyzing step involves selecting temporal changes chosen from the group consisting of those temporal changes which indicate an ever-increasing number of pixels in those identified areas of said cluster or those temporal changes which indicate that the increase in the size of said identified areas occurs to a greater extent in the top rather than the bottom part of said identified areas, and

wherein top and bottom portion of said identified areas being defined with respect to the direction of influence of gravity within said monitored area.

17. The method as recited in claim 14, wherein

said step of analyzing said cluster of identified areas involves making use of a neural network.