Anomaly detection with a moving camera using multiscale video analysis

This paper addresses the problem of abandoned object detection in a cluttered environment using a camera moving along a straight track. The developed system compares captured images to a previously recorded reference video, thus requiring proper temporal alignment and geometric registration between the two signals. A real-time constraint is imposed onto the system to allow an effective surveillance capability in practical situations. In this paper, we propose to deal with the simultaneous detection of objects of different sizes using a multiresolution approach together with normalized cross-correlation and a voting step. In order to develop and properly assess the proposed method we designed a database recorded in a real surveillance scenario, consisting of an industrial plant containing a large number of pipes and rotating machines. Also, we have devised a systematic parameter tuning routine that allows the system to be adapted to different scenarios. We have validated it using the designed database. The obtained results are quite effective, achieving real-time, robust abandoned object detection in an industrial plant scenario.