2 research outputs found
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart
Glasses, Computer Vision, Video Analytics, Human-machine Interactio
A bio-inspired knowledge representation method for anomaly detection in cognitive video surveillance systems
Human behaviour analysis has important applications
in the field of anomaly management, such as Intelligent
Video Surveillance (IVS). As the number of individuals in a scene
increases, however, new macroscopic complex behaviours emerge
from the underlying interaction network among multiple agents.
This phenomenon has lately been investigated by modelling such
interaction through Social Forces.
In most recent Intelligent Video Surveillance systems, mechanisms
to support human decisions are integrated in cognitive
artificial processes. These algorithms mainly address the problem
of modelling behaviours to allow for inference and prediction
over the environment. A bio-inspired structure is here proposed,
which is able to encode and synthesize signals, not only for the
description of single entities behaviours, but also for modelling
cause-effect relationships between user actions and changes in
environment configurations (i.e. the crowd). Such models are
stored within a memory during a learning phase. Here the system
operates an effective knowledge transfer from a human operator
towards an automatic systems called Cognitive Surveillance Node
(CSN), which is part of a complex cognitive JDL-based and bioinspired
architecture. After such a knowledge-transfer phase,
learned representations can be used, at different levels, either
to support human decisions by detecting anomalous interaction
models and thus compensating for human shortcomings, or, in
an automatic decision scenario, to identify anomalous patterns
and choose the best strategy to preserve stability of the entire
system.
Results are presented, where crowd behaviour is modelled by
means of Social Forces and can interact with a human operator
within a visual 3D simulator. The way anomalies are detected and
consequently handled is demonstrated on synthetic data and also
on a real video sequence, in both the user-support and automatic
modes