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Reduction of false alarms triggered by spiders/cobwebs in surveillance camera networks

Authors: 

Ramya Hebbalaguppe, Kevin McGuinness, Jogile Kuklyte, Rami Albatal, Cem Direkoglu, Noel O'Connor

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
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%.
Conference Name: 
2016 IEEE International Conference on Image Processing (ICIP)
Proceedings: 
2016 IEEE International Conference on Image Processing (ICIP)
Digital Object Identifer (DOI): 
10.1109/ICIP.2016.7532496
Publication Date: 
25/09/2016
Conference Location: 
United States of America
Research Group: 
Institution: 
Dublin City University (DCU)
Open access repository: 
Yes