REDUCTION OF FALSE ALARMS TRIGGERED BY SPIDERS/COBWEBS IN SURVEILLANCE CAMERA NETWORKS
23rd IEEE International Conference on Image Processing (ICIP), Arizona, United States Of America, 25 - 28 September 2016, pp.943-947, (Full Text)
- Publication Type: Conference Paper / Full Text
- Doi Number: 10.1109/icip.2016.7532496
- City: Arizona
- Country: United States Of America
- Page Numbers: pp.943-947
- Middle East Technical University Northern Cyprus Campus Affiliated: Yes
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%.