Akademik Veri Yönetim Sistemi
23rd IEEE International Conference on Image Processing (ICIP), Arizona, Amerika Birleşik Devletleri, 25 - 28 Eylül 2016, ss.943-947, (Tam Metin Bildiri)
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%.