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Anomaly Detection in Moving-Camera Video Sequences Using Principal Subspace Analysis

Thomaz, Lucas A. ; Jardim, Eric ; da Silva, Allan F. ; da Silva, Eduardo A. B. ; Netto, Sergio L. ; Krim, Hamid

IEEE transactions on circuits and systems. I, Regular papers, 2018-03, Vol.65 (3), p.1003-1015 [Periódico revisado por pares]

New York: IEEE

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Título:
Anomaly Detection in Moving-Camera Video Sequences Using Principal Subspace Analysis
Autor: Thomaz, Lucas A. ; Jardim, Eric ; da Silva, Allan F. ; da Silva, Eduardo A. B. ; Netto, Sergio L. ; Krim, Hamid
Assuntos: Algorithm design and analysis ; Algorithms ; Anomalies ; Anomaly detection ; Cameras ; Complexity ; Computation ; moving camera ; object detection ; OTHER INSTRUMENTATION ; Representations ; Robustness ; sparse representation ; subspace recovery ; Subspaces ; Surveillance ; Synchronism ; Synchronization ; Video anomaly detection ; Video sequences
É parte de: IEEE transactions on circuits and systems. I, Regular papers, 2018-03, Vol.65 (3), p.1003-1015
Notas: NA0002576
USDOE National Nuclear Security Administration (NNSA), Office of Nonproliferation and Verification Research and Development (NA-22)
Descrição: This paper presents a family of algorithms based on sparse decompositions that detect anomalies in video sequences obtained from slow moving cameras. These algorithms start by computing the union of subspaces that best represents all the frames from a reference (anomaly free) video as a low-rank projection plus a sparse residue. Then, they perform a low-rank representation of a target (possibly anomalous) video by taking advantage of both the union of subspaces and the sparse residue computed from the reference video. Such algorithms provide good detection results while at the same time obviating the need for previous video synchronization. However, this is obtained at the cost of a large computational complexity, which hinders their applicability. Another contribution of this paper approaches this problem by using intrinsic properties of the obtained data representation in order to restrict the search space to the most relevant subspaces, providing computational complexity gains of up to two orders of magnitude. The developed algorithms are shown to cope well with videos acquired in challenging scenarios, as verified by the analysis of 59 videos from the VDAO database that comprises videos with abandoned objects in a cluttered industrial scenario.
Editor: New York: IEEE
Idioma: Inglês

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Anomaly Detection in Moving-Camera Video Sequences Using Principal Subspace Analysis

Thomaz, Lucas A. ; Jardim, Eric ; da Silva, Allan F. ; da Silva, Eduardo A. B. ; Netto, Sergio L. ; Krim, Hamid

New York: IEEE

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