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Domain-Transformable Sparse Representation for Anomaly Detection in Moving-Camera Videos

Jardim, E. ; Thomaz, L. A. ; Silva, E. ; Netto, S. L.

IEEE Transactions on Image Processing Vol. 29, Nº -, pp. 1329 - 1343, September, 2020.

ISSN (print): 1057-7149
ISSN (online):

Scimago Journal Ranking: 1,78 (in 2020)

Digital Object Identifier: 10.1109/TIP.2019.2940686

This paper presents a special matrix factorization based on sparse representation that detects anomalies in video sequences generated with moving cameras. Such representation is made by associating the frames of the target video, that is a sequence to be tested for the presence of anomalies, with the frames of an anomaly-free reference video, which is a previously validated sequence. This factorization is done by a sparse coefficient matrix, and any target-video anomaly is encapsulated into a residue term. In order to cope with camera trepidations, domaintransformations are incorporated into the sparse representation process. Approximations of the transformed-domain optimization problem are introduced to turn it into a feasible iterative process. Results obtained from a comprehensive video database acquired with moving cameras on a visually cluttered environment indicate that the proposed algorithm provides a better geometric registration between reference and target videos, greatly improving the overall performance of the anomaly-detection system.