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

Abnormal event detection in crowded scenes using sparse representation

We propose to detect abnormal events via a sparse reconstruction over the normal bases. Given a collection of normal training examples, e.g., an image sequence or a collection of local spatio-temporal patches, we propose the sparse reconstruction cost (SRC) over the normal dictionary to measure the normalness of the testing sample. By introducing the prior weight of each basis during sparse reconstruction, the proposed SRC is more robust compared to other outlier detection criteria. To condense the over-completed normal bases into a compact dictionary, a novel dictionary selection method with group sparsity constraint is designed, which can be solved by standard convex optimization. Observing that the group sparsity also implies a low rank structure, we reformulate the problem using matrix decomposition, which can handle large scale training samples by reducing the memory requirement at each iteration from O(k2) to O(k) where k is the number of samples. We use the columnwise coordinate descent to solve the matrix decomposition represented formulation, which empirically leads to a similar solution to the group sparsity formulation. By designing different types of spatio-temporal basis, our method can detect both local and global abnormal events. Meanwhile, as it does not rely on object detection and tracking, it can be applied to crowded video scenes. By updating the dictionary incrementally, our method can be easily extended to online event detection. Experiments on three benchmark datasets and the comparison to the state-of-the-art methods validate the advantages of our method.Accepted versio

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

Common universal data structures (CUDS) and vocabulary in the SimPhoNy integrated framework

Advanced nano-enabled materials exhibit complex behaviour at all scales. Designing new materials requires that all properties are considered, down from the electronic and atomistic scales, where the atomistic arrangement and chemistry are relevant, to the micro-meter scale, where effects of extended defects and the microstructure are of concern, up to the macroscopic, device scales. Traditional multiscale approaches relay on separating the system into subdomains, each modelled separately by a suitable single scale method. Linking (hierarchical and sequential) and coupling (concurrent) multiscale models are then needed to allow for the information passage between subdomains. However, while numerous modelling methods and tools exists for modelling a material at a single scale, a.g., LAMMPS, Quantum ESPRESSO, or OpenFOAM, there is currently no well-established multiscale tools and approaches that can, for example, be easily adopted in Integrated Computational Material Engineering (ICME) tool chains. This is mainly due to the difficulty of designing monolithic multiscale applications that allow describing the material accurately at each subdomain or scale and at the same time enable the necessary linking and coupling. An integrated multiscale framework that facilitates interoperability between single scale available tools is therefore of great importance for designing new materials and devices, especially for nano-enabled systems

Visualization in the integrated SimPhoNy multiscale simulation framework

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