6,202 research outputs found
A proposal for Video Signature Tool and Video Fingerprinting
In this document we present and evaluate a video signature system, proposed by Signals and Communications Laboratory – Department of Electronic for Automation, University of Brescia (Italy). The proposed Video Signature is MPEG-7 compliant. The technology is based on the use of many different features (color, motion, etc.). The preliminary studies show that these features have relevant characteristics in their development in time. The testing results prove that the features act differently with respect of the type and modification the query suffered
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
Musical instrument classification using non-negative matrix factorization algorithms
In this paper, a class of algorithms for automatic classification of individual musical instrument sounds is presented. Several perceptual features used in general sound classification applications were measured for 300 sound recordings consisting of 6 different musical instrument classes (piano, violin, cello, flute, bassoon and soprano saxophone). In addition, MPEG-7 basic spectral and spectral basis descriptors were considered, providing an effective combination for accurately describing the spectral and timbrai audio characteristics. The audio flies were split using 70% of the available data for training and the remaining 30% for testing. A classifier was developed based on non-negative matrix factorization (NMF) techniques, thus introducing a novel application of NMF. The standard NMF method was examined, as well as its modifications: the local, the sparse, and the discriminant NMF. Experimental results are presented to compare MPEG-7 spectral basis representations with MPEG-7 basic spectral features alongside the various NMF algorithms. The results indicate that the use of the spectrum projection coefficients for feature extraction and the standard NMF classifier yields an accuracy exceeding 95%. ©2006 IEEE
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
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