172 research outputs found
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
Improving acoustic vehicle classification by information fusion
We present an information fusion approach for ground vehicle classification based on the emitted acoustic signal. Many acoustic factors can contribute to the classification accuracy of working ground vehicles. Classification relying on a single feature set may lose some useful information if its underlying sound production model is not comprehensive. To improve classification accuracy, we consider an information fusion diagram, in which various aspects of an acoustic signature are taken into account and emphasized separately by two different feature extraction methods. The first set of features aims to represent internal sound production, and a number of harmonic components are extracted to characterize the factors related to the vehicle’s resonance. The second set of features is extracted based on a computationally effective discriminatory analysis, and a group of key frequency components are selected by mutual information, accounting for the sound production from the vehicle’s exterior parts. In correspondence with this structure, we further put forward a modifiedBayesian fusion algorithm, which takes advantage of matching each specific feature set with its favored classifier. To assess the proposed approach, experiments are carried out based on a data set containing acoustic signals from different types of vehicles. Results indicate that the fusion approach can effectively increase classification accuracy compared to that achieved using each individual features set alone. The Bayesian-based decision level fusion is found fusion is found to be improved than a feature level fusion approac
Pattern mining approaches used in sensor-based biometric recognition: a review
Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems
Inferring Facial and Body Language
Machine analysis of human facial and body language is a challenging topic in computer
vision, impacting on important applications such as human-computer interaction and visual
surveillance. In this thesis, we present research building towards computational frameworks
capable of automatically understanding facial expression and behavioural body language.
The thesis work commences with a thorough examination in issues surrounding facial
representation based on Local Binary Patterns (LBP). Extensive experiments with different
machine learning techniques demonstrate that LBP features are efficient and effective for
person-independent facial expression recognition, even in low-resolution settings. We then
present and evaluate a conditional mutual information based algorithm to efficiently learn the
most discriminative LBP features, and show the best recognition performance is obtained by
using SVM classifiers with the selected LBP features. However, the recognition is performed
on static images without exploiting temporal behaviors of facial expression.
Subsequently we present a method to capture and represent temporal dynamics of facial
expression by discovering the underlying low-dimensional manifold. Locality Preserving Projections
(LPP) is exploited to learn the expression manifold in the LBP based appearance
feature space. By deriving a universal discriminant expression subspace using a supervised
LPP, we can effectively align manifolds of different subjects on a generalised expression manifold.
Different linear subspace methods are comprehensively evaluated in expression subspace
learning. We formulate and evaluate a Bayesian framework for dynamic facial expression
recognition employing the derived manifold representation. However, the manifold representation
only addresses temporal correlations of the whole face image, does not consider
spatial-temporal correlations among different facial regions. We then employ Canonical Correlation Analysis (CCA) to capture correlations among face
parts. To overcome the inherent limitations of classical CCA for image data, we introduce
and formalise a novel Matrix-based CCA (MCCA), which can better measure correlations in
2D image data. We show this technique can provide superior performance in regression and
recognition tasks, whilst requiring significantly fewer canonical factors. All the above work
focuses on facial expressions. However, the face is usually perceived not as an isolated object
but as an integrated part of the whole body, and the visual channel combining facial and
bodily expressions is most informative.
Finally we investigate two understudied problems in body language analysis, gait-based
gender discrimination and affective body gesture recognition. To effectively combine face
and body cues, CCA is adopted to establish the relationship between the two modalities, and
derive a semantic joint feature space for the feature-level fusion. Experiments on large data
sets demonstrate that our multimodal systems achieve the superior performance in gender
discrimination and affective state analysis.Research studentship of Queen Mary, the International Travel Grant of the Royal Academy of Engineering,
and the Royal Society International Joint Project
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