000 Allgemeines, Wissenschaft
Refine
Year of publication
Document Type
- Conference Proceeding (21)
- Article (11)
- Doctoral Thesis (1)
- Lecture (1)
- Report (1)
Has Fulltext
- no (35)
Is part of the Bibliography
- no (35)
Keywords
- Architektur (1)
- Computer Vision (1)
- Fahrerassistenzsystem (1)
We present a novel approach of distributing matrix multiplications among GPU-equipped nodes in a cluster system. In this context we discuss the induced challenges and possible solutions. Additionally we state an algorithm which outperforms optimized GPU BLAS libraries for small matrices. Furthermore we provide a novel theoretical model for distributing algorithms within homogeneous computation systems with multiple hierarchies. In the context of this model we develop an algorithm which can find the optimal distribution parameters for each involved subalgorithm. We provide a detailed analysis of the algorithms space and time complexities and justify its use with a structured evaluation within a small GPU-equipped Beowulf cluster.
We present a novel method to perform multi-class pattern classification with neural networks and test it on a challenging 3D hand gesture recognition problem. Our method consists of a standard one-against-all (OAA) classification, followed by another network layer classifying the resulting class scores, possibly augmented by the original raw input vector. This allows the network to disambiguate hard-to-separate classes as the distribution of class scores carries considerable information as well, and is in fact often used for assessing the confidence of a decision. We show that by this approach we are able to significantly boost our results, overall as well as for particular difficult cases, on the hard 10-class gesture classification task.
As smart homes are being more and more popular, the needs of finding assisting systems which interface between users and home environments are growing. Furthermore, for people living in such homes, elderly and disabled people in particular and others in general, it is totally important to develop devices, which can support and aid them in their ordinary daily life. We focused in this work on sustaining privacy issues of the user during a real interaction with the surrounding home environment. A smart person-specific assistant system for services in home environment is proposed. The role of this system is the assisting of persons by controlling home activities and guiding the adaption of Smart-Home-Human interface towards the needs of the considered person. At the same time the system sustains privacy issues of it’s interaction partner. As a special case of medical assisting the system is so implemented, that it provides for elderly or disabled people person-specific medical assistance . The system has the ability of identifying its interaction partner using some biometric features. According to the recognized ID the system, first, adopts towards the needs of recognized person. Second the system represents person-specific list of medicines either visually or auditive. And third the system gives an alarm in the case of taking medicament either later or earlier as normal taking time.
We present a light-weight real-time applicable 3D-gesture recognition system on mobile devices for improved Human-Machine Interaction. We utilize time-of-flight data coming from a single sensor and implement the whole gesture recognition pipeline on two different devices outlining the potential of integrating these sensors onto mobile devices. The main components are responsible for cropping the data to the essentials, calculation of meaningful features, training and classifying via neural networks and realizing a GUI on the device. With our system we achieve recognition rates of up to 98% on a 10-gesture set with frame rates reaching 20Hz, more than sufficient for any real-time applications.
We present a publicly available benchmark database for the problem of hand posture recognition from noisy depth data and fused RGB-D data obtained from low-cost time-of-flight (ToF) sensors. The database is the most extensive database of this kind containing over a million data samples (point clouds) recorded from 35 different individuals for ten different static hand postures. This captures a great amount of variance, due to person-related factors, but also scaling, translation and rotation are explicitly represented. Benchmark results achieved with a standard classification algorithm are computed by cross-validation both over samples and persons, the latter implying training on all persons but one and testing on the remaining one. An important result using this database is that cross-validation performance over samples (which is the standard procedure in machine learning) is systematically higher than cross-validation performance over persons, which is to our mind the true application-relevant measure of generalization performance.
Touch versus mid-air gesture interfaces in road scenarios-measuring driver performance degradation
(2016)
We present a study aimed at comparing the degradation of the driver's performance during touch gesture vs mid-air gesture use for infotainment system control. To this end, 17 participants were asked to perform the Lane Change Test. This requires each participant to steer a vehicle in a simulated driving environment while interacting with an infotainment system via touch and mid-air gestures. The decrease in performance is measured as the deviation from an optimal baseline. This study concludes comparable deviations from the baseline for the secondary task of infotainment interaction for both interaction variants. This is significant as all participants are experienced in touch interaction, however have had no experience at all with mid-air gesture interaction, favoring mid-air gestures for the long-term scenario.
Given the success of convolutional neural networks (CNNs) during recent years in numerous object recognition tasks, it seems logical to further extend their applicability to the treatment of three-dimensional data such as point clouds provided by depth sensors. To this end, we present an approach exploiting the CNN’s ability of automated feature generation and combine it with a novel 3D feature computation technique, preserving local information contained in the data. Experiments are conducted on a large data set of 600.000 samples of hand postures obtained via ToF (time-of-flight) sensors from 20 different persons, after an extensive parameter search in order to optimize network structure. Generalization performance, measured by a leave-one-person-out scheme, exceeds that of any other method presented for this specific task, bringing the error for some persons down to 1.5 %.
Checking wind turbines for damage is a common problem for operators of wind parks, as regular inspections are legally required in many countries and prevention is economically viable. While some of the common forms of damage are easily visible on the surface, structural problems can remain invisible for years before they eventually result in catastrophic failure of a rotor blade. Common forms of testing fibre composite parts like ultrasonic testing or X-ray tests are impractical due to the large dimensions of wind turbine components and their limited accessibility for any short-range methods. Active thermographic inspection of wind turbines is a promising approach to testing for structural flaws beneath the surface of rotor blades. As part of an ongoing research project, a setup for testing the general viability of this method was built and used to compare different thermographic cameras. A sample cut from a discarded rotor blade was modified to emulate structural damage. The results are promising for the development of a cost effective on-site testing system.
In this review, we describe current Machine Learning approaches to hand gesture recognition with depth data from time-of-flight sensors. In particular, we summarise the achievements on a line of research at the Computational Neuroscience laboratory at the Ruhr West University of Applied Sciences. Relating our results to the work of others in this field, we confirm that Convolutional Neural Networks and Long Short-Term Memory yield most reliable results. We investigated several sensor data fusion techniques in a deep learning framework and performed user studies to evaluate our system in practice. During our course of research, we gathered and published our data in a novel benchmark dataset (REHAP), containing over a million unique three-dimensional hand posture samples.
We present a system for 3D hand gesture recognition based on low-cost time-of-flight(ToF) sensors intended for outdoor use in automotive human-machine interaction. As signal quality is impaired compared to Kinect-type sensors, we study several ways to improve performance when a large number of gesture classes is involved. Our system fuses data coming from two ToF sensors which is used to build up a large database and subsequently train a multilayer perceptron (MLP). We demonstrate that we are able to reliably classify a set of ten hand gestures in real-time and describe the setup of the system, the utilised methods as well as possible application scenarios.
With the introduction of Apple’s iPhone, gesture control became pop-
ular and was perceived as an intuitive means of interaction. Contact-
less gestures received broad attention with the X-Box Kinect.
Current technology is limited to a small number of uses, mainly
in entertainment systems. The target of this project is to increase the
range of possible applications, e.g. to the field of automotive,
industrial applications (manufacturing plants), assisted living in con-
texts ranging from private households to hospitals (interaction for
people with disabilities) and many more.
With a rapidly ageing population, it is increasingly important to de-
velop devices for elderly and disabled people that can support and aid
them in their daily lives, helping them to live at home as long as pos-
sible. The goal of this project is to implement a human-machine inter-
action and assistance system that can offer personalised health sup-
port for elderly people, or for those who have special needs in the
home environment.
As smart homes are being more and more popular, the needs of finding assisting systems which interface between users and home environments are growing. Furthermore, for elderly and disabled people living in such homes it is totally important to develop devices, which can support and aid them in their ordinary daily life. This demands means and tools that extend independent living and promote improved health. In this work we reviewed the state of the art in the assistant systems in home environments. A case study of medical assisting system for elderly and people with disabilities is discussed deeply. A smart nfc-based person-specific assistant system for services in home environment is proposed. The role of this system is the assisting by controlling of home activities and adaption of home-human interface towards the needs of the considered person. For the special case of medical assisting the system has the ability of providing for elderly or disabled people person-specific medical assistance. The system has the ability of identifying its interaction partner using some biometric features. According to the recognized ID the system, first, adopts towards the needs of recognized person. Second the system represents person-specific list of medicaments either visually, on screen, or acoustic, speaker. And third the system gives an alarm in the case of taking medicament either later or earlier as normal taking time.
In the context of existing approaches to cluster computing we present a newly developed modular framework `SimpleHydra' for rapid deployment and management of Beowulf clusters. Instead of focusing only the pure computation tasks on homogeneous clusters (i.e. clusters with identically set up nodes), this framework aims to ease the configuration of heterogeneous clusters and to provide a low-level / high-level object-oriented API for low-latency distributed computing. Our framework does not make any restrictions regarding the hardware and minimizes the use of external libraries to the case of special modules. In addition to that our framework enables the user to develop highly dynamic cluster topologies. We describe the framework's general structure as well as time critical elements, give application examples in the `Big-Data' context during a research project and briefly discuss additional features. Furthermore we give a thorough theoretical time/space complexity analysis of our implemented methods and general approaches.
In this paper, we describe an efficient method for a fast people re-identification based on models of human clothes. An initial model is estimated during people detection and tracking, which will be refined during the re-identification. This stepwise extraction, combination and comparing of features speeds up the whole re-identification. For the refining, several saliency maps are used to extract individual features. These individual features are located separately for any human body part. The body parts are located with an optimized GPU-based HOG detector. Furthermore, we introduce a meanshift-based fusion concept which utilizes multiple detectors in order to increase the detection reliability.
Currently in home environments, robot assisting systems with emotion understanding ability are generally achieved in two several manners. The first is the implementing of such systems in such a way that they offer general services for all considered persons without considering privacy, special needs of their interaction partners. The second way is the targetting of such systems for merely one person. In this work we present a robot assisting system, which has both the abilities of assisting several persons at the same time and sustaining their privacy and security issues. The robot can interact with it's interaction partner emotionally by analyzing the emotions of her expressed either visually, facial expression, or auditive, speech prosody. The role of this system is the providing of person-specific support in home environment. In order to identify its interaction partner the system uses diverse biometric traits. According to the recognized ID the system, first, adopts towards the needs of recognized person. Second the system loads the corresponding emotional profile of the detected interaction partner in order to practice a person-specific emotional human-robot interaction, which has an advantage over the person independent interaction.