5,140 research outputs found
LCrowdV: Generating Labeled Videos for Simulation-based Crowd Behavior Learning
We present a novel procedural framework to generate an arbitrary number of
labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to
design accurate algorithms or training models for crowded scene understanding.
Our overall approach is composed of two components: a procedural simulation
framework for generating crowd movements and behaviors, and a procedural
rendering framework to generate different videos or images. Each video or image
is automatically labeled based on the environment, number of pedestrians,
density, behavior, flow, lighting conditions, viewpoint, noise, etc.
Furthermore, we can increase the realism by combining synthetically-generated
behaviors with real-world background videos. We demonstrate the benefits of
LCrowdV over prior lableled crowd datasets by improving the accuracy of
pedestrian detection and crowd behavior classification algorithms. LCrowdV
would be released on the WWW
Spatio-temporal Video Parsing for Abnormality Detection
Abnormality detection in video poses particular challenges due to the
infinite size of the class of all irregular objects and behaviors. Thus no (or
by far not enough) abnormal training samples are available and we need to find
abnormalities in test data without actually knowing what they are.
Nevertheless, the prevailing concept of the field is to directly search for
individual abnormal local patches or image regions independent of another. To
address this problem, we propose a method for joint detection of abnormalities
in videos by spatio-temporal video parsing. The goal of video parsing is to
find a set of indispensable normal spatio-temporal object hypotheses that
jointly explain all the foreground of a video, while, at the same time, being
supported by normal training samples. Consequently, we avoid a direct detection
of abnormalities and discover them indirectly as those hypotheses which are
needed for covering the foreground without finding an explanation for
themselves by normal samples. Abnormalities are localized by MAP inference in a
graphical model and we solve it efficiently by formulating it as a convex
optimization problem. We experimentally evaluate our approach on several
challenging benchmark sets, improving over the state-of-the-art on all standard
benchmarks both in terms of abnormality classification and localization.Comment: 15 pages, 12 figures, 3 table
Violent behaviour detection using local trajectory response
Surveillance systems in the United Kingdom are prominent,
and the number of installed cameras is estimated to be around
1.8 million. It is common for a single person to watch multiple
live video feeds when conducting active surveillance, and
past research has shown that a person’s effectiveness at successfully
identifying an event of interest diminishes the more
monitors they must observe. We propose using computer vision
techniques to produce a system that can accurately identify
scenes of violent behaviour. In this paper we outline three
measures of motion trajectory that when combined produce a
response map that highlights regions within frames that contain
behaviour typical of violence based on local information.
Our proposed method demonstrates state-of-the-art classification
ability when given the task of distinguishing between violent
and non-violent behaviour across a wide variety of violent
data, including real-world surveillance footage obtained from
local police organisations
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
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