A scrubbing technique for the automatic detection of victims in urban search and rescue video

Authors:
Martin Gerdzhev;Jimmy Tran;Alexander Ferworn;Kevin Barnum;Mike Dolderman
Affiliations:
Ryerson University, Toronto, Ontario, Canada;Ryerson University, Toronto, Ontario, Canada;Ryerson University, Toronto, Ontario, Canada;Provincial Emergency Response Team, Bolton, Ontario, Canada;Provincial Emergency Response Team, Bolton, Ontario, Canada
Venue:
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
Year:
2010

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Abstract

In the discipline of Urban Search and Rescue (US&R), the faster a live human can found the more likely their rescue will be successful with success being measured in lives saved. We have been working to augment trained US&R dogs with technology to help first responders in the US&R effort and give them a better understanding of the condition of the disaster area being searched and the trapped people who are found. As one can imagine, the video feed from a dog can be quite jittery. We have been exploring ways to speed the process of video "scrubbing" by automatically discarding segments of video which show nothing interesting and concentrating on segments that are critical. This paper discusses one of these techniques.