Reduction of false alarms triggered by spiders/cobwebs in surveillance camera networks.
Hebbalaguppe, Ramya, McGuinness, KevinORCID: 0000-0003-1336-6477, Kuklyte, Jogile, Albatal, RamiORCID: 0000-0002-9269-8578, Direkoglu, Cem and O'Connor, Noel E.ORCID: 0000-0002-4033-9135
(2016)
Reduction of false alarms triggered by spiders/cobwebs in surveillance camera networks.
In: IEEE International Conference on Image Processing, 25-28 Sep 2016, Phoenix, AZ.
ISBN 978-1-4673-9661-6/16
The percentage of false alarms caused by spiders in automated surveillance can range from 20-50%. False alarms increase the workload of surveillance personnel validating the alarms and the maintenance labor cost associated with regular cleaning of webs. We propose a novel, cost effective method to detect false alarms triggered by spiders/webs in surveillance camera networks. This is accomplished by building a spider
classifier intended to be a part of the surveillance video processing pipeline. The proposed method uses a feature descriptor obtained by early fusion of blur and texture. The approach is sufficiently efficient for real-time processing and yet comparable in performance with more computationally costly approaches like SIFT with bag of visual words aggregation.
The proposed method can eliminate 98.5% of false
alarms caused by spiders in a data set supplied by an industry partner, with a false positive rate of less than 1%