Human CCTV operators face several challenges in their task which can lead to missed events, people or associations,
including: (a) data overload in large distributed multi-camera environments; (b) short attention span;
(c) limited knowledge of what to look for; and (d) lack of access to non-visual contextual intelligence to aid
search. Developing a system to aid human operators and alleviate such burdens requires addressing the problem
of automatic re-identification of people across disjoint camera views, a matching task made difficult by factors
such as lighting, viewpoint and pose changes and for which absolute scoring approaches are not best suited.
Accordingly, we describe a distributed multi-camera tracking (MCT) system to visually aid human operators in
associating people and objects effectively over multiple disjoint camera views in a large public space. The system
comprises three key novel components: (1) relative measures of ranking rather than absolute scoring to learn the
best features for matching; (2) multi-camera behaviour profiling as higher-level knowledge to reduce the search
space and increase the chance of finding correct matches; and (3) human-assisted data mining to interactively
guide search and in the process recover missing detections and discover previously unknown associations. We
provide an extensive evaluation of the greater effectiveness of the system as compared to existing approaches on
industry-standard i-LIDS multi-camera data.
This paper aims to address the problem of behavioural anomaly detection in surveillance videos. We propose
a novel framework tailored towards global video behaviour anomaly detection in complex outdoor
scenes involving multiple temporal processes caused by correlated behaviours of multiple objects. Specifically,
given a complex wide-area scene that has been segmented automatically into semantic regions where
behaviour patterns are represented as discrete local atomic events, we formulate a novel Cascade of Dynamic
Bayesian Networks (CasDBNs) to model behaviours with complex temporal correlations by utilising
combinatory evidences collected from local atomic events. Using a cascade configuration not only allows for
accurate detection of video behaviour anomalies, more importantly, it also improves the robustness of the
model in dealing with the inevitable presence of errors and noise in the behaviour representation resulting
less false alarms. We evaluate the effectiveness of the proposed framework on a real world traffic scene.
The results demonstrate that the framework is able to detect not only anomalies that are visually obvious,
but also those that are ambiguous or supported only by very weak visual evidence, e.g. those that can be
easily missed by a human observer.