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Gait recognition using HMMs and dual discriminative observations for sub-dynamics analysis
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.We propose a new gait recognition method that combines holistic and model-based features. Both types of features are extracted automatically from gait silhouette sequences and their combination takes place by means of a pair of hidden Markov models. In the proposed system, the holistic features are initially used for capturing general gait dynamics whereas, subsequently, the model-based features are deployed for capturing more detailed sub-dynamics by refining upon the preceding general dynamics. Furthermore, the holistic and model-based features are suitably processed in order to improve the discriminatory capacity of the final system. The experimental results show that the proposed method exhibits performance advantages in comparison with popular existing methods
Gait recognition with shifted energy image and structural feature extraction
This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.In this paper, we present a novel and efficient gait recognition system. The proposed system uses two novel gait representations, i.e., the shifted energy image and the gait structural profile, which have increased robustness to some classes of structural variations. Furthermore, we introduce a novel method for the simulation of walking conditions and the generation of artificial subjects that are used for the application of linear discriminant analysis. In the decision stage, the two representations are fused. Thorough experimental evaluation, conducted using one traditional and two new databases, demonstrates the advantages of the proposed system in comparison with current state-of-the-art systems
Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation
Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions
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
Learning optimised representations for view-invariant gait recognition
Gait recognition can be performed without subject cooperation under harsh conditions, thus it is an important tool in forensic gait analysis, security control, and other commercial applications. One critical issue that prevents gait recognition systems from being widely accepted is the performance drop when the camera viewpoint varies between the registered templates and the query data. In this paper, we explore the potential of combining feature optimisers and representations learned by convolutional neural networks (CNN) to achieve efficient view-invariant gait recognition. The experimental results indicate that CNN learns highly discriminative representations across moderate view variations, and these representations can be further improved using view-invariant feature selectors, achieving a high matching accuracy across views
Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.
Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems
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