656 research outputs found
Anomaly detection in moving-camera videos with sparse and low-rank matrix decompositions
This work presents two methods based on sparse decompositions that can detect anomalies in video sequences obtained from moving cameras. The first method starts by computing the union of subspaces (UoS) that best represents all the frames from a reference (anomaly-free) video as a low-rank projection plus a sparse residue. Then it performs a low-rank representation of the target (possibly anomalous) video by taking advantage of both the UoS and the sparse residue computed from the reference video. The anomalies are extracted after post-processing this video with these residual data. Such algorithm provides good detection results while at the same time obviating the need for previous video synchronization. However, this technique looses its detection efficiency when target and reference videos presents more severe misalignments. This may happen due to small uncontrolled camera moviment and shaking during the acquisition phase, which is often common in realworld situations. To extend its applicability, a second contribution is proposed in order to cope with these possible pose misalignments. This is done by modeling the target-reference pose discrepancy as geometric transformations acting on the domain of frames of the target video. A complete matrix decomposition algorithm is presented in order to perform a sparse representation of the target video as a sparse combination of the reference video plus a sparse residue, while taking into account the transformation acting on it. Our method is then verified and compared against state-of-the-art techniques using a challenging video dataset, that comprises recordings presenting the described misalignments. Under the evaluation metrics used, the second proposed method exhibits an improvement of at least 16% over the first proposed one, and 22% over the next best rated method.Apresentamos dois mĂ©todos baseados em decomposições esparsas que podem detectar anomalias em sequĂŞncias de vĂdeo obtidas por câmeras em movimento. O primeiro mĂ©todo estima a uniĂŁo de subespaços (UdS) que melhor representa todos os quadros de um vĂdeo de referĂŞncia (livre de anomalias) como uma projeção de baixo-posto mais um resĂduo esparso. Em seguida, Ă© realizada uma representação de baixo-posto do vĂdeo alvo (possivelmente anĂ´malo) aproveitando a UdS e o resĂduo esparso calculado a partir do vĂdeo de referĂŞncia. As anomalias sĂŁo extraĂdas apĂłs o pĂłs-processamento destas informações residuais. Esse algoritmo fornece bons resultados de detecção, alĂ©m de eliminar a necessidade de uma sincronização prĂ©via dos vĂdeos. No entanto, essa tĂ©cnica perde eficiĂŞncia quando os vĂdeos de referĂŞncia e alvo apresentam desalinhamentos mais graves entre si. Isso pode ocorrer devido a pequenos movimentos descontrolados da câmera e tremores durante a fase de aquisição. Para estender sua aplicabilidade, uma segunda contribuição Ă© proposta a fim de lidar com esse possĂvel desalinhamento. Isso Ă© feito modelando a discrepância de pose de câmera entre os vĂdeos de referĂŞncia e alvo com transformações geomĂ©tricas agindo no domĂnio dos quadros do vĂdeo alvo. Um algoritmo completo de decomposição de matrizes Ă© apresentado para realizar uma representação esparsa do vĂdeo alvo como uma combinação esparsa do vĂdeo de referĂŞncia, levando em consideração as transformações que atuam sobre seus quadros. Nosso mĂ©todo Ă© entĂŁo verificado e comparado com tĂ©cnicas de Ăşltima geração com auxĂlio de vĂdeos de uma base desafiadora, apresentando os desalinhamentos em questĂŁo. Sob as mĂ©tricas de avaliação usadas, o segundo mĂ©todo proposto exibe uma melhoria de pelo menos 16% em relação ao primeiro, e 22% sobre o mĂ©todo melhor avaliado logo em seguida
Automated Intruder Detection from Image Sequences using Minimum Volume Sets
We propose a new algorithm based on machine learning techniques for automatic intruder detection in surveillance networks. The algorithm is theoretically founded on the concept of minimum volume sets. Through application to image sequences from two different scenarios and comparison with some existing algorithms, we show that it is possible for our proposed algorithm to easily obtain high detection accuracy with low false alarm rates
Subspace discovery for video anomaly detection
PhDIn automated video surveillance anomaly detection is a challenging task. We address
this task as a novelty detection problem where pattern description is limited
and labelling information is available only for a small sample of normal instances.
Classification under these conditions is prone to over-fitting. The contribution of this
work is to propose a novel video abnormality detection method that does not need
object detection and tracking. The method is based on subspace learning to discover
a subspace where abnormality detection is easier to perform, without the need of
detailed annotation and description of these patterns. The problem is formulated as
one-class classification utilising a low dimensional subspace, where a novelty classifier
is used to learn normal actions automatically and then to detect abnormal actions
from low-level features extracted from a region of interest. The subspace is discovered
(using both labelled and unlabelled data) by a locality preserving graph-based algorithm
that utilises the Graph Laplacian of a specially designed parameter-less nearest
neighbour graph.
The methodology compares favourably with alternative subspace learning algorithms
(both linear and non-linear) and direct one-class classification schemes commonly
used for off-line abnormality detection in synthetic and real data. Based on
these findings, the framework is extended to on-line abnormality detection in video
sequences, utilising multiple independent detectors deployed over the image frame to
learn the local normal patterns and infer abnormality for the complete scene. The
method is compared with an alternative linear method to establish advantages and
limitations in on-line abnormality detection scenarios. Analysis shows that the alternative
approach is better suited for cases where the subspace learning is restricted on
the labelled samples, while in the presence of additional unlabelled data the proposed
approach using graph-based subspace learning is more appropriate
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