This thesis work focusses upon developing the capability to automatically evaluate
and detect anomalies in human behaviour from surveillance video. We work with
static monocular cameras in crowded urban surveillance scenarios, particularly air-
ports and commercial shopping areas. Typically a person is 100 to 200 pixels high
in a scene ranging from 10 - 20 meters width and depth, populated by 5 to 40 peo-
ple at any given time. Our procedure evaluates human behaviour unobtrusively to
determine outlying behavioural events,
agging abnormal events to the operator.
In order to achieve automatic human behaviour anomaly detection we address
the challenge of interpreting behaviour within the context of the social and physical
environment. We develop and evaluate a process for measuring social connectivity
between individuals in a scene using motion and visual attention features. To do this
we use mutual information and Euclidean distance to build a social similarity matrix
which encodes the social connection strength between any two individuals. We de-
velop a second contextual basis which acts by segmenting a surveillance environment
into behaviourally homogeneous subregions which represent high tra c slow regions
and queuing areas. We model the heterogeneous scene in homogeneous subgroups
using both contextual elements. We bring the social contextual information, the
scene context, the motion, and visual attention features together to demonstrate
a novel human behaviour anomaly detection process which nds outlier behaviour
from a short sequence of video. The method, Nearest Neighbour Ranked Outlier
Clusters (NN-RCO), is based upon modelling behaviour as a time independent se-
quence of behaviour events, can be trained in advance or set upon a single sequence.
We nd that in a crowded scene the application of Mutual Information-based social
context permits the ability to prevent self-justifying groups and propagate anomalies
in a social network, granting a greater anomaly detection capability. Scene context
uniformly improves the detection of anomalies in all the datasets we test upon.
We additionally demonstrate that our work is applicable to other data domains.
We demonstrate upon the Automatic Identi cation Signal data in the maritime
domain. Our work is capable of identifying abnormal shipping behaviour using joint
motion dependency as analogous for social connectivity, and similarly segmenting
the shipping environment into homogeneous regions
