3 research outputs found
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
Video event segmentation and visualisation in non-linear subspace
We introduce the use of dimensionality reduction for video event detection without explicitly using motion estimation or object tracking. Raw data from video sequences are used to construct a low dimensional mapping representing the input frames. We compare Principal Component Analysis, Multidimensional Scaling, Isomap, Maximum Variance Unfolding and Laplacian Eigenmaps and implement an approach based on local, non-linear dimensionality reduction. We propose an approach with a graph based on the similarity of frames and enriched with the temporal information from the sequence processed by Laplacian Eigenmaps. This makes it possible to visualise the manifold of motion in the scene and to detect unusual events in a low dimensional space. We demonstrate the approach on standard traffic surveillance test sequences. Key words: unusual event detection, dimensionality reduction, laplacian eigenmaps 1