1,006 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
Proof-of-Concept
Biometry is an area in great expansion and is considered as possible solution to cases where high
authentication parameters are required. Although this area is quite advanced in theoretical
terms, using it in practical terms still carries some problems. The systems available still depend
on a high cooperation level to achieve acceptable performance levels, which was the backdrop
to the development of the following project. By studying the state of the art, we propose the
creation of a new and less cooperative biometric system that reaches acceptable performance
levels.A constante necessidade de parΓ’metros mais elevados de seguranΓ§a, nomeadamente ao nΓvel
de autenticação, leva ao estudo biometria como possΓvel solução. Actualmente os mecanismos
existentes nesta Γ‘rea tem por base o conhecimento de algo que se sabe βpasswordβ ou algo
que se possui βcodigo Pinβ. Contudo este tipo de informação Γ© facilmente corrompida ou contornada.
Desta forma a biometria é vista como uma solução mais robusta, pois garante que a
autenticação seja feita com base em medidas fΓsicas ou compartimentais que definem algo que
a pessoa Γ© ou faz (βwho you areβ ou βwhat you doβ).
Sendo a biometria uma solução bastante promissora na autenticação de indivΓduos, Γ© cada vez
mais comum o aparecimento de novos sistemas biomΓ©tricos. Estes sistemas recorrem a medidas
fΓsicas ou comportamentais, de forma a possibilitar uma autenticação (reconhecimento) com
um grau de certeza bastante considerΓ‘vel. O reconhecimento com base no movimento do corpo
humano (gait), feiçáes da face ou padrΓ΅es estruturais da Γris, sΓ£o alguns exemplos de fontes
de informação em que os sistemas actuais se podem basear. Contudo, e apesar de provarem
um bom desempenho no papel de agentes de reconhecimento autΓ³nomo, ainda estΓ£o muito
dependentes a nΓvel de cooperação exigida. Tendo isto em conta, e tudo o que jΓ‘ existe no
ramo do reconhecimento biometrico, esta Γ‘rea estΓ‘ a dar passos no sentido de tornar os seus
mΓ©todos o menos cooperativos poss??veis. Possibilitando deste modo alargar os seus objectivos
para além da mera autenticação em ambientes controlados, para casos de vigilÒncia e controlo
em ambientes nΓ£o cooperativos (e.g. motins, assaltos, aeroportos).
Γ nesta perspectiva que o seguinte projecto surge. AtravΓ©s do estudo do estado da arte, pretende
provar que Γ© possΓvel criar um sistema capaz de agir perante ambientes menos cooperativos,
sendo capaz de detectar e reconhecer uma pessoa que se apresente ao seu alcance.O
sistema proposto PAIRS (Periocular and Iris Recognition Systema) tal como nome indica, efectua
o reconhecimento atravΓ©s de informação extraΓda da Γris e da regiΓ£o periocular (regiΓ£o circundante
aos olhos). O sistema Γ© construΓdo com base em quatro etapas: captura de dados,
prΓ©-processamento, extração de caracterΓsticas e reconhecimento. Na etapa de captura de
dados, foi montado um dispositivo de aquisição de imagens com alta resolução com a capacidade
de capturar no espectro NIR (Near-Infra-Red). A captura de imagens neste espectro tem
como principal linha de conta, o favorecimento do reconhecimento atravΓ©s da Γris, visto que
a captura de imagens sobre o espectro visΓvel seria mais sensΓvel a variaçáes da luz ambiente.
Posteriormente a etapa de prΓ©-processamento implementada, incorpora todos os mΓ³dulos do
sistema responsÑveis pela detecção do utilizador, avaliação de qualidade de imagem e segmentação
da Γris. O modulo de detecção Γ© responsΓ‘vel pelo desencadear de todo o processo, uma
vez que esta é responsÑvel pela verificação da exist?ncia de um pessoa em cena. Verificada
a sua exist?ncia, sΓ£o localizadas as regiΓ΅es de interesse correspondentes ? Γris e ao periocular,
sendo tambΓ©m verificada a qualidade com que estas foram adquiridas. ConcluΓdas estas
etapas, a Γris do olho esquerdo Γ© segmentada e normalizada. Posteriormente e com base em
vΓ‘rios descritores, Γ© extraΓda a informação biomΓ©trica das regiΓ΅es de interesse encontradas,
e Γ© criado um vector de caracterΓsticas biomΓ©tricas. Por fim, Γ© efectuada a comparação dos
dados biometricos recolhidos, com os jÑ armazenados na base de dados, possibilitando a criação
de uma lista com os nΓveis de semelhanΓ§a em termos biometricos, obtendo assim um resposta
final do sistema. ConcluΓda a implementação do sistema, foi adquirido um conjunto de imagens capturadas atravΓ©s do sistema implementado, com a participação de um grupo de voluntΓ‘rios.
Este conjunto de imagens permitiu efectuar alguns testes de desempenho, verificar e afinar
alguns parΓ’metros, e proceder a optimização das componentes de extração de caracterΓsticas e
reconhecimento do sistema. Analisados os resultados foi possΓvel provar que o sistema proposto
tem a capacidade de exercer as suas funçáes perante condiçáes menos cooperativas
The Design and Implementation of an Image Segmentation System for Forest Image Analysis
The United States Forest Service (USFS) is developing software systems to evaluate forest resources with respect to qualities such as scenic beauty and vegetation structure. Such evaluations usually involve a large amount of human labor. In this thesis, I will discuss the design and implementation of a digital image segmentation system, and how to apply it to analyze forest images so that automated forest resource evaluation can be achieved. The first major contribution of the thesis is the evaluation of various feature design schemes for segmenting forest images. The other major contribution of this thesis is the development of a pattern recognition-based image segmentation algorithm. The best system performance was a 61.4% block classification error rate, achieved by combining color histograms with entropy. This performance is better than that obtained by an ?intelligent? guess based on prior knowledge about the categories under study, which is 68.0%
Person re-Identification over distributed spaces and time
PhDReplicating the human visual system and cognitive abilities that the brain uses to process the
information it receives is an area of substantial scientific interest. With the prevalence of video
surveillance cameras a portion of this scientific drive has been into providing useful automated
counterparts to human operators. A prominent task in visual surveillance is that of matching
people between disjoint camera views, or re-identification. This allows operators to locate people
of interest, to track people across cameras and can be used as a precursory step to multi-camera
activity analysis. However, due to the contrasting conditions between camera views and their
effects on the appearance of people re-identification is a non-trivial task. This thesis proposes
solutions for reducing the visual ambiguity in observations of people between camera views
This thesis first looks at a method for mitigating the effects on the appearance of people under
differing lighting conditions between camera views. This thesis builds on work modelling
inter-camera illumination based on known pairs of images. A Cumulative Brightness Transfer
Function (CBTF) is proposed to estimate the mapping of colour brightness values based on limited
training samples. Unlike previous methods that use a mean-based representation for a set of
training samples, the cumulative nature of the CBTF retains colour information from underrepresented
samples in the training set. Additionally, the bi-directionality of the mapping function
is explored to try and maximise re-identification accuracy by ensuring samples are accurately
mapped between cameras.
Secondly, an extension is proposed to the CBTF framework that addresses the issue of changing
lighting conditions within a single camera. As the CBTF requires manually labelled training
samples it is limited to static lighting conditions and is less effective if the lighting changes. This
Adaptive CBTF (A-CBTF) differs from previous approaches that either do not consider lighting
change over time, or rely on camera transition time information to update. By utilising contextual
information drawn from the background in each camera view, an estimation of the lighting
change within a single camera can be made. This background lighting model allows the mapping
of colour information back to the original training conditions and thus remove the need for
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retraining.
Thirdly, a novel reformulation of re-identification as a ranking problem is proposed. Previous
methods use a score based on a direct distance measure of set features to form a correct/incorrect
match result. Rather than offering an operator a single outcome, the ranking paradigm is to give
the operator a ranked list of possible matches and allow them to make the final decision. By utilising
a Support Vector Machine (SVM) ranking method, a weighting on the appearance features
can be learned that capitalises on the fact that not all image features are equally important to
re-identification. Additionally, an Ensemble-RankSVM is proposed to address scalability issues
by separating the training samples into smaller subsets and boosting the trained models.
Finally, the thesis looks at a practical application of the ranking paradigm in a real world application.
The system encompasses both the re-identification stage and the precursory extraction
and tracking stages to form an aid for CCTV operators. Segmentation and detection are combined
to extract relevant information from the video, while several combinations of matching
techniques are combined with temporal priors to form a more comprehensive overall matching
criteria.
The effectiveness of the proposed approaches is tested on datasets obtained from a variety
of challenging environments including offices, apartment buildings, airports and outdoor public
spaces
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