On the detection of abandoned objects with a moving camera using robust subspace recovery and sparse representation
We consider the application of sparse-representation and robust-subspace-recovery techniques to detect abandoned objects in a target video acquired with a moving camera. In the proposed framework, the target video is compared to a previously acquired reference video, which is assumed to have no abandoned objects. The detection method explores the low-rank similarities among the reference and target videos, as well as the sparsity of the differences between the two video sequences caused by the unexpected object in the target video. A three-step procedure is then presented adapting a previous low-rank and sparse image representation to the problem at hand. Performance of the proposed technique is verified using a large video database for abandoned-object detection in a cluttered environment. Results demonstrate the technique effectiveness even in the presence of some significant camera shake along its trajectory.