1,412 research outputs found
Covariate conscious approach for Gait recognition based upon Zernike moment invariants
Gait recognition i.e. identification of an individual from his/her walking
pattern is an emerging field. While existing gait recognition techniques
perform satisfactorily in normal walking conditions, there performance tend to
suffer drastically with variations in clothing and carrying conditions. In this
work, we propose a novel covariate cognizant framework to deal with the
presence of such covariates. We describe gait motion by forming a single 2D
spatio-temporal template from video sequence, called Average Energy Silhouette
image (AESI). Zernike moment invariants (ZMIs) are then computed to screen the
parts of AESI infected with covariates. Following this, features are extracted
from Spatial Distribution of Oriented Gradients (SDOGs) and novel Mean of
Directional Pixels (MDPs) methods. The obtained features are fused together to
form the final well-endowed feature set. Experimental evaluation of the
proposed framework on three publicly available datasets i.e. CASIA dataset B,
OU-ISIR Treadmill dataset B and USF Human-ID challenge dataset with recently
published gait recognition approaches, prove its superior performance.Comment: 11 page
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
From a Visual Scene to a Virtual Representation: A Cross-Domain Review
The widespread use of smartphones and other low-cost equipment as recording devices, the
massive growth in bandwidth, and the ever-growing demand for new applications with enhanced capabilities,
made visual data a must in several scenarios, including surveillance, sports, retail, entertainment, and
intelligent vehicles. Despite significant advances in analyzing and extracting data from images and video,
there is a lack of solutions able to analyze and semantically describe the information in the visual scene
so that it can be efficiently used and repurposed. Scientific contributions have focused on individual
aspects or addressing specific problems and application areas, and no cross-domain solution is available
to implement a complete system that enables information passing between cross-cutting algorithms. This
paper analyses the problem from an end-to-end perspective, i.e., from the visual scene analysis to the
representation of information in a virtual environment, including how the extracted data can be described
and stored. A simple processing pipeline is introduced to set up a structure for discussing challenges and
opportunities in different steps of the entire process, allowing to identify current gaps in the literature.
The work reviews various technologies specifically from the perspective of their applicability to an endto-
end pipeline for scene analysis and synthesis, along with an extensive analysis of datasets for relevant
tasks.info:eu-repo/semantics/publishedVersio
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