16 research outputs found
Analysis of time domain information for footstep recognition
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-17289-2_47Proceedings of 6th International Symposium, ISVC 2010, Las Vegas, NV, (USA)This paper reports an experimental analysis of footsteps as a biometric. The focus here is on information extracted from the time domain of signals collected from an array of piezoelectric sensors. Results are related to the largest footstep database collected to date, with almost 20,000 valid footstep signals and more than 120 persons, which is well beyond previous related databases. Three feature approaches have been extracted, the popular ground reaction force (GRF), the spatial average and the upper and lower contours of the pressure signals. Experimental work is based on a verification mode with a holistic approach based on PCA and SVM, achieving results in the range of 5 to 15% EER depending on the experimental conditions of quantity of data used in the reference models.R.V.-R., J.F. and J.O.-G. are supported by projects Contexts (S2009/TIC-1485), Bio-Challenge (TEC2009-11186), TeraSense (CSD2008-00068) and "Cátedra UAM-Telefónica"
Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture
Deep neural networks are applied to a wide range of problems in recent years.
In this work, Convolutional Neural Network (CNN) is applied to the problem of
determining the depth from a single camera image (monocular depth). Eight
different networks are designed to perform depth estimation, each of them
suitable for a feature level. Networks with different pooling sizes determine
different feature levels. After designing a set of networks, these models may
be combined into a single network topology using graph optimization techniques.
This "Semi Parallel Deep Neural Network (SPDNN)" eliminates duplicated common
network layers, and can be further optimized by retraining to achieve an
improved model compared to the individual topologies. In this study, four SPDNN
models are trained and have been evaluated at 2 stages on the KITTI dataset.
The ground truth images in the first part of the experiment are provided by the
benchmark, and for the second part, the ground truth images are the depth map
results from applying a state-of-the-art stereo matching method. The results of
this evaluation demonstrate that using post-processing techniques to refine the
target of the network increases the accuracy of depth estimation on individual
mono images. The second evaluation shows that using segmentation data alongside
the original data as the input can improve the depth estimation results to a
point where performance is comparable with stereo depth estimation. The
computational time is also discussed in this study.Comment: 44 pages, 25 figure
Obstacle Detection and Avoidance System Based on Monocular Camera and Size Expansion Algorithm for UAVs
One of the most challenging problems in the domain of autonomous aerial vehicles is the designing of a robust real-time obstacle detection and avoidance system. This problem is complex, especially for the micro and small aerial vehicles, that is due to the Size, Weight and Power (SWaP) constraints. Therefore, using lightweight sensors (i.e., Digital camera) can be the best choice comparing with other sensors; such as laser or radar. For real-time applications, different works are based on stereo cameras in order to obtain a 3D model of the obstacles, or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the collision state of the approaching obstacles using monocular camera is proposed. The key of the proposed algorithm is to analyze the size changes of the detected feature points, combined with the expansion ratios of the convex hull constructed around the detected feature points from consecutive frames. During the Aerial Vehicle (UAV) motion, the detection algorithm estimates the changes in the size of the area of the approaching obstacles. First, the method detects the feature points of the obstacles, then extracts the obstacles that have the probability of getting close toward the UAV. Secondly, by comparing the area ratio of the obstacle and the position of the UAV, the method decides if the detected obstacle may cause a collision. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV performs the avoidance maneuver. The proposed algorithm was evaluated by performing real indoor and outdoor flights, and the obtained results show the accuracy of the proposed algorithm compared with other related works.Research supported by the Spanish Government through the Cicyt project ADAS ROAD-EYE (TRA2013-48314-C3-1-R)
Air Force Institute of Technology Research Report 2020
This Research Report presents the FY20 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document
Radar and RGB-depth sensors for fall detection: a review
This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing
Biologically motivated keypoint detection for RGB-D data
With the emerging interest in active vision, computer vision researchers have been increasingly
concerned with the mechanisms of attention. Therefore, several visual attention
computational models inspired by the human visual system, have been developed, aiming at
the detection of regions of interest in images.
This thesis is focused on selective visual attention, which provides a mechanism for the
brain to focus computational resources on an object at a time, guided by low-level image properties
(Bottom-Up attention). The task of recognizing objects in different locations is achieved
by focusing on different locations, one at a time. Given the computational requirements of the
models proposed, the research in this area has been mainly of theoretical interest. More recently,
psychologists, neurobiologists and engineers have developed cooperation's and this has
resulted in considerable benefits. The first objective of this doctoral work is to bring together
concepts and ideas from these different research areas, providing a study of the biological research
on human visual system and a discussion of the interdisciplinary knowledge in this area, as
well as the state-of-art on computational models of visual attention (bottom-up). Normally, the
visual attention is referred by engineers as saliency: when people fix their look in a particular
region of the image, that's because that region is salient. In this research work, saliency methods
are presented based on their classification (biological plausible, computational or hybrid)
and in a chronological order.
A few salient structures can be used for applications like object registration, retrieval or
data simplification, being possible to consider these few salient structures as keypoints when
aiming at performing object recognition. Generally, object recognition algorithms use a large
number of descriptors extracted in a dense set of points, which comes along with very high computational
cost, preventing real-time processing. To avoid the problem of the computational
complexity required, the features have to be extracted from a small set of points, usually called
keypoints. The use of keypoint-based detectors allows the reduction of the processing time and
the redundancy in the data. Local descriptors extracted from images have been extensively
reported in the computer vision literature. Since there is a large set of keypoint detectors, this
suggests the need of a comparative evaluation between them. In this way, we propose to do a
description of 2D and 3D keypoint detectors, 3D descriptors and an evaluation of existing 3D keypoint
detectors in a public available point cloud library with 3D real objects. The invariance of
the 3D keypoint detectors was evaluated according to rotations, scale changes and translations.
This evaluation reports the robustness of a particular detector for changes of point-of-view and
the criteria used are the absolute and the relative repeatability rate. In our experiments, the
method that achieved better repeatability rate was the ISS3D method.
The analysis of the human visual system and saliency maps detectors with biological inspiration
led to the idea of making an extension for a keypoint detector based on the color
information in the retina. Such proposal produced a 2D keypoint detector inspired by the behavior
of the early visual system. Our method is a color extension of the BIMP keypoint detector,
where we include both color and intensity channels of an image: color information is included
in a biological plausible way and multi-scale image features are combined into a single keypoints
map. This detector is compared against state-of-art detectors and found particularly
well-suited for tasks such as category and object recognition. The recognition process is performed
by comparing the extracted 3D descriptors in the locations indicated by the keypoints after mapping the 2D keypoints locations to the 3D space. The evaluation allowed us to obtain
the best pair keypoint detector/descriptor on a RGB-D object dataset. Using our keypoint detector
and the SHOTCOLOR descriptor a good category recognition rate and object recognition
rate were obtained, and it is with the PFHRGB descriptor that we obtain the best results.
A 3D recognition system involves the choice of keypoint detector and descriptor. A new
method for the detection of 3D keypoints on point clouds is presented and a benchmarking is
performed between each pair of 3D keypoint detector and 3D descriptor to evaluate their performance
on object and category recognition. These evaluations are done in a public database
of real 3D objects. Our keypoint detector is inspired by the behavior and neural architecture
of the primate visual system: the 3D keypoints are extracted based on a bottom-up 3D saliency
map, which is a map that encodes the saliency of objects in the visual environment. The saliency
map is determined by computing conspicuity maps (a combination across different modalities)
of the orientation, intensity and color information, in a bottom-up and in a purely stimulusdriven
manner. These three conspicuity maps are fused into a 3D saliency map and, finally, the
focus of attention (or "keypoint location") is sequentially directed to the most salient points in
this map. Inhibiting this location automatically allows the system to attend to the next most
salient location. The main conclusions are: with a similar average number of keypoints, our 3D
keypoint detector outperforms the other eight 3D keypoint detectors evaluated by achiving the
best result in 32 of the evaluated metrics in the category and object recognition experiments,
when the second best detector only obtained the best result in 8 of these metrics. The unique
drawback is the computational time, since BIK-BUS is slower than the other detectors. Given
that differences are big in terms of recognition performance, size and time requirements, the
selection of the keypoint detector and descriptor has to be matched to the desired task and we
give some directions to facilitate this choice. After proposing the 3D keypoint detector, the research focused on a robust detection and
tracking method for 3D objects by using keypoint information in a particle filter. This method
consists of three distinct steps: Segmentation, Tracking Initialization and Tracking. The segmentation
is made to remove all the background information, reducing the number of points for
further processing. In the initialization, we use a keypoint detector with biological inspiration.
The information of the object that we want to follow is given by the extracted keypoints. The
particle filter does the tracking of the keypoints, so with that we can predict where the keypoints
will be in the next frame. In a recognition system, one of the problems is the computational cost
of keypoint detectors with this we intend to solve this problem. The experiments with PFBIKTracking
method are done indoors in an office/home environment, where personal robots are
expected to operate. The Tracking Error evaluates the stability of the general tracking method.
We also quantitatively evaluate this method using a "Tracking Error". Our evaluation is done by
the computation of the keypoint and particle centroid. Comparing our system that the tracking
method which exists in the Point Cloud Library, we archive better results, with a much smaller
number of points and computational time. Our method is faster and more robust to occlusion
when compared to the OpenniTracker.Com o interesse emergente na visão ativa, os investigadores de visão computacional têm
estado cada vez mais preocupados com os mecanismos de atenção. Por isso, uma série de
modelos computacionais de atenção visual, inspirado no sistema visual humano, têm sido desenvolvidos.
Esses modelos têm como objetivo detetar regiões de interesse nas imagens.
Esta tese está focada na atenção visual seletiva, que fornece um mecanismo para que
o cérebro concentre os recursos computacionais num objeto de cada vez, guiado pelas propriedades
de baixo nível da imagem (atenção Bottom-Up). A tarefa de reconhecimento de
objetos em diferentes locais é conseguida através da concentração em diferentes locais, um
de cada vez. Dados os requisitos computacionais dos modelos propostos, a investigação nesta
área tem sido principalmente de interesse teórico. Mais recentemente, psicólogos, neurobiólogos
e engenheiros desenvolveram cooperações e isso resultou em benefícios consideráveis. No
início deste trabalho, o objetivo é reunir os conceitos e ideias a partir dessas diferentes áreas
de investigação. Desta forma, é fornecido o estudo sobre a investigação da biologia do sistema
visual humano e uma discussão sobre o conhecimento interdisciplinar da matéria, bem como
um estado de arte dos modelos computacionais de atenção visual (bottom-up). Normalmente,
a atenção visual é denominada pelos engenheiros como saliência, se as pessoas fixam o olhar
numa determinada região da imagem é porque esta região é saliente. Neste trabalho de investigação,
os métodos saliência são apresentados em função da sua classificação (biologicamente
plausível, computacional ou híbrido) e numa ordem cronológica.
Algumas estruturas salientes podem ser usadas, em vez do objeto todo, em aplicações
tais como registo de objetos, recuperação ou simplificação de dados. É possível considerar
estas poucas estruturas salientes como pontos-chave, com o objetivo de executar o reconhecimento
de objetos. De um modo geral, os algoritmos de reconhecimento de objetos utilizam um
grande número de descritores extraídos num denso conjunto de pontos. Com isso, estes têm um
custo computacional muito elevado, impedindo que o processamento seja realizado em tempo
real. A fim de evitar o problema da complexidade computacional requerido, as características
devem ser extraídas a partir de um pequeno conjunto de pontos, geralmente chamados pontoschave.
O uso de detetores de pontos-chave permite a redução do tempo de processamento e a
quantidade de redundância dos dados. Os descritores locais extraídos a partir das imagens têm
sido amplamente reportados na literatura de visão por computador. Uma vez que existe um
grande conjunto de detetores de pontos-chave, sugere a necessidade de uma avaliação comparativa
entre eles. Desta forma, propomos a fazer uma descrição dos detetores de pontos-chave
2D e 3D, dos descritores 3D e uma avaliação dos detetores de pontos-chave 3D existentes numa
biblioteca de pública disponível e com objetos 3D reais. A invariância dos detetores de pontoschave
3D foi avaliada de acordo com variações nas rotações, mudanças de escala e translações.
Essa avaliação retrata a robustez de um determinado detetor no que diz respeito às mudanças
de ponto-de-vista e os critérios utilizados são as taxas de repetibilidade absoluta e relativa. Nas
experiências realizadas, o método que apresentou melhor taxa de repetibilidade foi o método
ISS3D.
Com a análise do sistema visual humano e dos detetores de mapas de saliência com inspiração
biológica, surgiu a ideia de se fazer uma extensão para um detetor de ponto-chave
com base na informação de cor na retina. A proposta produziu um detetor de ponto-chave 2D
inspirado pelo comportamento do sistema visual. O nosso método é uma extensão com base na cor do detetor de ponto-chave BIMP, onde se incluem os canais de cor e de intensidade de
uma imagem. A informação de cor é incluída de forma biológica plausível e as características
multi-escala da imagem são combinadas num único mapas de pontos-chave. Este detetor
é comparado com os detetores de estado-da-arte e é particularmente adequado para tarefas
como o reconhecimento de categorias e de objetos. O processo de reconhecimento é realizado
comparando os descritores 3D extraídos nos locais indicados pelos pontos-chave. Para isso, as
localizações do pontos-chave 2D têm de ser convertido para o espaço 3D. Isto foi possível porque
o conjunto de dados usado contém a localização de cada ponto de no espaço 2D e 3D. A avaliação
permitiu-nos obter o melhor par detetor de ponto-chave/descritor num RGB-D object dataset.
Usando o nosso detetor de ponto-chave e o descritor SHOTCOLOR, obtemos uma noa taxa de
reconhecimento de categorias e para o reconhecimento de objetos é com o descritor PFHRGB
que obtemos os melhores resultados.
Um sistema de reconhecimento 3D envolve a escolha de detetor de ponto-chave e descritor,
por isso é apresentado um novo método para a deteção de pontos-chave em nuvens de
pontos 3D e uma análise comparativa é realizada entre cada par de detetor de ponto-chave
3D e descritor 3D para avaliar o desempenho no reconhecimento de categorias e de objetos.
Estas avaliações são feitas numa base de dados pública de objetos 3D reais. O nosso detetor
de ponto-chave é inspirado no comportamento e na arquitetura neural do sistema visual dos
primatas. Os pontos-chave 3D são extraídas com base num mapa de saliências 3D bottom-up,
ou seja, um mapa que codifica a saliência dos objetos no ambiente visual. O mapa de saliência
é determinada pelo cálculo dos mapas de conspicuidade (uma combinação entre diferentes
modalidades) da orientação, intensidade e informações de cor de forma bottom-up e puramente
orientada para o estímulo. Estes três mapas de conspicuidade são fundidos num mapa de saliência
3D e, finalmente, o foco de atenção (ou "localização do ponto-chave") está sequencialmente
direcionado para os pontos mais salientes deste mapa. Inibir este local permite que o sistema
automaticamente orientado para próximo local mais saliente. As principais conclusões são: com
um número médio similar de pontos-chave, o nosso detetor de ponto-chave 3D supera os outros
oito detetores de pontos-chave 3D avaliados, obtendo o melhor resultado em 32 das métricas
avaliadas nas experiências do reconhecimento das categorias e dos objetos, quando o segundo
melhor detetor obteve apenas o melhor resultado em 8 dessas métricas. A única desvantagem
é o tempo computacional, uma vez que BIK-BUS é mais lento do que os outros detetores. Dado
que existem grandes diferenças em termos de desempenho no reconhecimento, de tamanho
e de tempo, a seleção do detetor de ponto-chave e descritor tem de ser interligada com a
tarefa desejada e nós damos algumas orientações para facilitar esta escolha neste trabalho de
investigação.
Depois de propor um detetor de ponto-chave 3D, a investigação incidiu sobre um método
robusto de deteção e tracking de objetos 3D usando as informações dos pontos-chave num filtro
de partículas. Este método consiste em três etapas distintas: Segmentação, Inicialização do
Tracking e Tracking. A segmentação é feita de modo a remover toda a informação de fundo,
a fim de reduzir o número de pontos para processamento futuro. Na inicialização, usamos um
detetor de ponto-chave com inspiração biológica. A informação do objeto que queremos seguir
é dada pelos pontos-chave extraídos. O filtro de partículas faz o acompanhamento dos pontoschave,
de modo a se poder prever onde os pontos-chave estarão no próximo frame. As experiências
com método PFBIK-Tracking são feitas no interior, num ambiente de escritório/casa, onde
se espera que robôs pessoais possam operar. Também avaliado quantitativamente este método
utilizando um "Tracking Error". A avaliação passa pelo cálculo das centróides dos pontos-chave e
das partículas. Comparando o nosso sistema com o método de tracking que existe na biblioteca usada no desenvolvimento, nós obtemos melhores resultados, com um número muito menor de
pontos e custo computacional. O nosso método é mais rápido e mais robusto em termos de
oclusão, quando comparado com o OpenniTracker