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Moving-Camera Video Surveillance in Cluttered Environments Using Deep Features

Afonso, B. M. ; Cinelli, L. P. ; Thomaz, L. A. ; Silva, A. F. ; Silva, E. ; Netto, S. L.

Moving-Camera Video Surveillance in Cluttered Environments Using Deep Features, Proc IEEE International Conference on Image Processing ICIP, Athens, Greece, Vol. , pp. 2296 - 2300, October, 2018.

Digital Object Identifier: 10.1109/ICIP.2018.8451540

Abstract
This paper deals with the challenging problem of visual anomaly detection in a cluttered environment using videos acquired with a moving camera. The anomalies considered are abandoned objects. A new method is proposed for comparing two videos (an anomaly-free reference video and a target one possibly with anomalies) by using convolutional neural networks as feature extractors for a subsequent anomaly-detection stage using a classifier. Two classifier strategies are considered, namely a fully-connected neural network and a random forest algorithm. Results for a comprehensive abandoned object database acquired with a moving camera in a cluttered environment indicate that the proposed architecture can match even the state-of-the-art algorithms in terms of object-detection performance, with a reduction in processing time of 80%.