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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.
DamokleS 4.0
(2019)
Dieser interne Bericht beschreibt die Zielsetzung, Durchführung und Auswertung des Projektes Damokles 4.0. Das Projekt zielt darauf ab, neue, digitale Technologien in die Schwerindustrie einzuführen um Produktionsprozesse zu modernisieren. Unter Einsatz neuer Technologien, insbesondere mobiler Geräte, soll ein cyberphyiskalisches System (CPS) eine kontextbasierte und künstlich intelligente Unterstützung der Mitarbeiter in den Bereichen der Schwerindustrie ermöglichen. Hierzu werden typische Anwendungsfälle und die damit verbundenen Szenarien zur Unterstützung der Mitarbeiter auf Basis von neuen, flexiblen, adaptiven und mobilen Technologien, wie Augmented Reality und künstlicher Intelligenz, modelliert. Um den Prototypen einer AR-Anwendung und einer kamerabasierte Personenverfolgung zu entwickeln, hat die Hochschule Ruhr West im kleinen Technikum am Campus Bottrop eine entsprechende industrielle Umgebung simuliert. Die Projektergebnisse zeigen die Anwendbarkeit der vorgeschlagenen Softwareansätze und die Ergebnisse einer Untersuchung der psychologischen Einflüsse auf die Mitarbeiter.
For face recognition from video streams speed and accuracy are vital aspects. The first decision whether a preprocessed image region represents a human face or not is often made by a feed-forward neural network (NN), e.g. in the Viisage-FaceFINDER® video surveillance system. We describe the optimisation of such a NN by a hybrid algorithm combining evolutionary multi-objective optimisation (EMO) and gradient-based learning. The evolved solutions perform considerably faster than an expert-designed architecture without loss of accuracy. We compare an EMO and a single objective approach, both with online search strategy adaptation. It turns out that EMO is preferable to the single objective approach in several respects.
Im vorliegenden Beitrag wird ein hochsprachenprogrammierbares System zur schritthaltenden Vollbild-Interpretation natürlich beleuchteter Szenenfolgen im Videotakt vorgestellt. Im einzelnen werden folgende Teilmodule und Subsysteme beschrieben: eine hochdynamische, pixellokal autoadaptive CMOS-Kamera mit ca. 120 dB Helligkeitsdynamik (20Bits/Pixel) ein hochsprachenprogrammierbarer Systolic Array Prozessor (für die pixelbezogenen Verarbeitungsmodule) im PCI-Kartenformat, samt optimierendem Compiler, Simulator und Emulator Systemprozeßgerüste unter Linux auf den für die Echtzeit-Anwendungen eingesetzten Hostrechnern (z.B. DEC/Alpha oder Intel/ Pentium)eine prototypische Anwendung zur bildverarbeitungsbasierten Eigenbewegungsbeobachtung (Translationsrichtung, Eotationsraten)eine prototypische, automotive Anwendung zur schritthalt enden Detektion und Kartierung des Straßen- und Spurverlaufs unter partieller monokularer 3D-Rekonstruktion, sowie prototypische Anwendungen zur Klassifikation verkehrsrelevanter Hindernisse (Verkehrsteilnehmer)
Coming out of the labs, the first robots are currently appearing on the consumer market. Initially they target rather simple application scenarios ranging from entertainment to home convenience. However, one can expect, that they will capture more complex areas soon. These robots will have a higher and higher level and a broad range of functional competence, and will collaborate and interactively communicate with their human users. All this requires considerable cognitive abilities on the robot’s side and appropriate man-machine interaction technologies. Apart from further development of individual functions and technologies it is crucial to build and evaluate fully integrated systems. This paper describes our approach to construct a robotic assistance system. We present experience with an integrated technology demonstration and the exposure of the integrated system to the public.
The first robots are currently appearing on the consumer market. Initially they are targeted at rather simple applications such as entertainment and home convenience. For more complex areas, these robots will need to collaborate and interactively communicate with their human users, which requires appropriate man-machine interaction technologies and considerable cognitive abilities on the robot's side. Consumer acceptance will strongly depend on the integrated system. Thus, system integration and evaluation of the integrated system is becoming increasingly important. This paper describes our approach to construct a robotic assistance system. We present experience with an integrated technology demonstration and exposure of the integrated system to the public.
Multimodaler Sensor zur Fahrzeugführung: Teilprojekt: Architektur, Rundumsicht und Objekterkennung
(1997)
Systeme zur automatisierten Bildanalyse sind vielfältig einsetzbar und gewinnen aufgrund technologischer Weiterentwicklungen und gesellschaftlicher Akzeptanz zunehmend an Bedeutung. Schwerpunkt im Bereich der "Technischen Bildverarbeitung dynamischer Szenen" ist die Entwicklung von Methoden, die bei der Interpretation von Bildern aus verschiedenen Sensordaten Verwendung finden. Dies sind neben den herkömmlichen Kamerabildern im wesentlichen Röntgen- und Radarbilder. Unter geeigneter Berücksichtigung der durch die jeweiligen Anwendungen vorgegebenen Randbedingungen werden daraus entsprechende Verfahren abgeleitet. Derzeitige Projekte beschäftigen sich mit der Analyse von Straßenverkehrsszenen, der Detektion von Sprengstoffzündern bei der Durchleuchtung von Fluggepäck, sowie mit der Bestimmung von Art und Ausdehnung von Ölverschmutzungen bei der Meeresüberwachung.
Systems for automated image analysis are useful for a variety of tasks and their importance is still increasing due to technological advances and an increase of social acceptance. The main focus of "Technical Image Processing of Dynamic Scenes" lies
with the development of methods for the interpretation of images derived from various sensors. Apart from conventional visual images, this involves mainly X-ray and radar images. Taking into account the requirements of the various applications, suitable methods are derived. Current projects are dealing with the analysis of traffic scenes, detection of detonators when X-raying luggage and determination of type and expansion of oil pollution in maritime surveillance.
To reduce the number of traffic accidents and to increase the drivers comfort, the thought of designing driver assistance systems arose in the past years. Fully or partly autonomously guided vehicles, particularly for road traffic, pose high demands on the development of reliable algorithms. Principal problems are caused by having a moving observer in predominantly natural environments. At the Institut fur Neuroinformatik methods for analyzing driving relevant scenes by computer vision are developed in cooperation with several partners from the automobile industry. We present a solution for a driver assistance system. We concentrate on the aspects of video-based scene analysis and organization of behavior.
Das vorliegende Paper gibt einen Überblick über das Verhalten von modernen, autonom navigierenden Fahrzeugen in Baustellen. Dabei werden besondere Herausforderungen für die autonome Navigation im Baustellenbereich benannt. Außerdem wird ein Überblick über die Sensorausstattung und die Fahrerassistenzsysteme von modernen Fahrzeugen gegeben und es werden Technologien vorgestellt, die für eine Verbesserung der autonomen Navigation durch Baustellen genutzt werden können. Es wird ein Versuch durchgeführt, der aufzeigt, wie zuverlässig moderne Fahrzeuge durch Baustellensituationen navigieren können. Dabei werden Schwachstellen, wie bspw. die mangelnde Verfügbarkeit von Fahrerassistenzsystemen bei niedrigen Geschwindigkeiten, aufgedeckt.
Autonomous driving is one of the future visions in which many vehicle manufacturers are working with high pressure.
Nowadays, it is already supported partially by high-class vehicles. A completely autonomous journey is indeed the goal, but in cars for
the public road traffic still not available. Automatic lane keeping assistants, speed regulators as well as shield and obstacle detections
are parts or precursors on the way to completely autonomous driving.
The American vehicle manufacturer Tesla is not only known for its electric drive, but also for the fact that high-pressure work is carried out on the autonomous drive. Tesla is thus the only vehicle manufacturer to use its users as so-called beta testers for its assistance systems. The progress and the function of the currently available Model S in the field of assistance systems and autonomic driving is documented and described in this paper. It is shown how good or bad the test vehicle manages scenarios in normal road traffic situations
with the assistance systems, e.g. lane keeping assistant, speed control, lane change and distance assistant, and which scenarios can
not be managed by the vehicle itself.
Relax yourself - Using Virtual Reality to enhance employees mental health and work performance
(2019)
This paper presents work-in-progress aiming to develop an actively adapting virtual reality (VR) relaxation application. Due to the immersive nature of VR technologies, people can escape from their real environment and get into a relaxing state. Goal of the application is to adapt to the users' physiological signals to foster the positive effect. Until now, a first version of the VR application was constructed and is currently evaluated in an experiment. Preliminary results of this study demonstrate that people appreciate the immersion into the virtual environment and escape from reality. Moreover, participants highlighted the option to adapt users' needs and preferences. Based on the final study data, the constructed application will be enhanced with regard to adoption and surrounding factors.
Increasing economic viability and safety through structural health monitoring of wind turbines
(2017)
Serious accidents with property damage or even human casualties, result from structural flaws in wind turbine rotor blades. Common maintenance practices result in long downtimes and do not lead to the required results. Therefore, the Ruhr West University of Applied Sciences and the iQbis Consulting GmbH, currently research a new structural health monitoring method for wind turbine rotor blades. The goal of this project is to build a sensor system that can detect structural weaknesses inside of rotor blades without the need of downtime for industrial climbers. This technology has the potential to prevent accidents, save lives, extend the useful life of wind turbines and optimize the production of green energy.
Für das sichere Führen von Fahrzeugen im Straßenverkehr ist ein hohes Maß an Informationsverarbeitung notwendig, um aus den zur Verfügung stehenden Informationen, Handlungen für die Fahrzeugsteuerung abzuleiten. Der Mensch löst diese Aufgabe hauptsächlich auf der Basis visueller Informationen. Durch die Arbeitsweise des menschlichen Gehirns motiviert, wird am Institut für Neuroinformatik der Ruhr-Universität Bochum an einer Fahrzeugführung mittels Computer Vision gearbeitet. Fortlaufend oder zumindest in kurzen Abständen müssen hierbei Verkehrsteilnehmer aus den visuellen Informationen extrahiert und danach weiter attributiert werden. Wichtige Eigenschaften sind hierbei: Objektklasse (PKW, LKW, Fußgänger etc.), Abstand, Geschwindigkeit, Bewegungsrichtung und das Gefahrenpotential bezüglich der eigenen Ortsveränderung. Die Vielzahl der durch die Umwelt aufgestellten Randbedingungen und das aus der Aufgabenstellung implizierte hohe Maß an Sicherheit bedingen ein robustes und flexibles Gesamtsystem. Dieses Gesamtsystem besteht zum einen aus Basis-Algorithmen zur Vorverarbeitung der Eingabedaten und Extraktion von Bildmerkmalen und zum anderen aus darauf aufbauenden Verfahren zur Segmentierung, Klassifizierung und Verfolgung von Fahrzeugen.
We present a pipeline for recognizing dynamic freehand gestures on mobile devices based on extracting depth information coming from a single Time-of-Flight sensor. Hand gestures are recorded with a mobile 3D sensor, transformed frame by frame into an appropriate 3D descriptor and fed into a deep LSTM network for recognition purposes. LSTM being a recurrent neural model, it is uniquely suited for classifying explicitly time-dependent data such as hand gestures. For training and testing purposes, we create a small database of four hand gesture classes, each comprising 40 × 150 3D frames. We conduct experiments concerning execution speed on a mobile device, generalization capability as a function of network topology, and classification ability ‘ahead of time’, i.e., when the gesture is not yet completed. Recognition rates are high (>95%) and maintainable in real-time as a single classification step requires less than 1 ms computation time, introducing freehand gestures for mobile systems.
In this contribution we present a novel approach to transform data from time-of-flight (ToF) sensors to be interpretable by Convolutional Neural Networks (CNNs). As ToF data tends to be overly noisy depending on various factors such as illumination, reflection coefficient and distance, the need for a robust algorithmic approach becomes evident. By spanning a three-dimensional grid of fixed size around each point cloud we are able to transform three-dimensional input to become processable by CNNs. This simple and effective neighborhood-preserving methodology demonstrates that CNNs are indeed able to extract the relevant information and learn a set of filters, enabling them to differentiate a complex set of ten different gestures obtained from 20 different individuals and containing 600.000 samples overall. Our 20-fold cross-validation shows the generalization performance of the network, achieving an accuracy of up to 98.5% on validation sets comprising 20.000 data samples. The real-time applicability of our system is demonstrated via an interactive validation on an infotainment system running with up to 40fps on an iPad in the vehicle interior.
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.
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.
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.
Recognition of emotions from multimodal cues is of basic interest for the design of many adaptive interfaces in human-machine interaction (HMI) in general and human-robot interaction (HRI) in particular. It provides a means to incorporate non-verbal feedback in the course of interaction. Humans express their emotional and affective state rather unconsciously exploiting their different natural communication modalities such as body language, facial expression and prosodic intonation. In order to achieve applicability in realistic HRI settings, we develop person-independent affective models. In this paper, we present a study on multimodal recognition of emotions from such auditive and visual cues for interaction interfaces. We recognize six classes of basic emotions plus the neutral one of talking persons. The focus hereby lies on the simultaneous online visual and accoustic analysis of speaking faces. A probabilistic decision level fusion scheme based on Bayesian networks is applied to draw benefit of the complementary information from both – the acoustic and the visual – cues. We compare the performance of our state of the art recognition systems for separate modalities to the improved results after applying our fusion scheme on both DaFEx database and a real-life data that captured directly from robot. We furthermore discuss the results with regard to the theoretical background and future applications.
Utilizing biometrie traits for privacy- and security-applications is receiving an increasing attention. Applications such as personal identification, access control, forensics appli-cations, e-banking, e-government, e-health and recently person-alized human-smart-home and human-robot interaction present some examples. In order to offer person-specific services for/of specific person a pre-identifying step should be done in the run-up. Using biometric in such application is encountered by diverse challenges. First, using one trait and excluding the others depends on the application aimed to. Some applications demand directly touch to biometric sensors, while others don't. Second challenge is the reliability of used biometric arrangement. Civilized application demands lower reliability comparing to the forensics ones. And third, for biometric system could only one trait be used (uni-modal systems) or multiple traits (Bi- or Multi-modal systems). The latter is applied, when systems with a relative high reliability are expected. The main aim of this paper is providing a comprehensive view about biometric and its application. The above mentioned challenges will be analyzed deeply. The suitability of each biometric sensor according to the aimed application will be deeply discussed. Detailed com-parison between uni-modal and Multi-modal biometric system will present which system where to be utilized. Privacy and security issues of biometric systems will be discussed too. Three scenarios of biometric application in home-environment, human-robot-interaction and e-health will be presented.
Das kEFIR‐Projekt untersucht die praktische Anwendung von thermographischen Verfahren zur Analyse der strukturellen Integrität von Windkraftrotorblättern. Das Projekt entstand in Zusammenarbeit der Hochschule Ruhr West (HRW) mit der IQbis Consulting GmbH im Rahmen eines ZIM‐Förderprojekts des Bundesministeriums für Wirtschaft und Energie (BMWi). Hintergrund ist die zunehmende Anzahl von Windkraftanlagen (WKA) und der somit steigende Wartungsaufwand. Um einen reibungslosen Betrieb dieser Anlagen zu gewährleisten, und damit den besonderen Anforderungen an die Verfügbarkeit energieerzeugender Anlagen sicherzustellen, ist ein Bedarf an qualitativ hochwertigen Fehleranalysesystemen für im Betrieb befindlicher WKA von besonderer Bedeutung. Erfahrungsgemäß ist der Zeitaufwand für diese Inspektionen mit aktuellen Mitteln sehr groß und wird üblicherweise mit mehreren Arbeitstagen kalkuliert. Die Reproduzierbarkeit der gewonnenen Daten ist bei den derzeitigen Methoden meist nicht gewährleistet. Um frühzeitig auf Instabilitäten oder Schäden in den Rotorblättern einer WKA aufmerksam zu werden, ist die Entwicklung eines schnellen und qualitativ hochwertigen Fehleranalysesystems von zentraler Bedeutung. Ein Forschungsschwerpunkt in diesem Zusammenhang ist die Entwicklung von geeigneten bildgebenden und berührungslosen Verfahren, welche bei den Inspektionen eingesetzt werden können. Beispielsweise erlaubt der Einsatz thermographischer Sensoren eine Analyse nicht nur der Rotorblattoberfläche, sondern auch ihrer inneren Struktur. Weiterhin ist aufgrund des schnell wachsenden Marktes bei unbemannten Luftfahrzeugen, wie beispielsweise positionsstabiler Quatrocoptersysteme, eine zusätzliche Möglichkeit gegeben, die Inspektion von Windenergieanlagen mit Hilfe mobiler, kompakter und fliegender Analysesysteme zu unterstützen.
Applying step heating thermography to wind turbine rotor blades as a non-destructive testing method
(2017)
A self-driving car that operates on the SAE automation level 3 or 4 can navigate through different traffic conditions without human input. If such a system is on its operating limits, it will emit a takeover request before shutting down. This request will likely generate a physical response of the driver. Our goal is to shed light on the stress perception of drivers in various scenarios. To this end, we have carried out a feasibility study for preparation. Two subjects drove an autonomous vehicle and during the ride ECG signals were recorded, and afterwards evaluated. Unfortunately, the stress reaction to takeover requests could not be investigated, due to the poor function of the autonomous driving mode from the vehicle, however the reaction to autopilot misconduct without warning to the driver could be investigated instead.
Practical application of object detection systems, in research or industry, favors highly optimized black box solutions. We show how such a highly optimized system can be further augmented in terms of its reliability with only a minimal increase of computation times, i.e. preserving realtime boundaries. Our solution leaves the initial (HOG-based) detector unchanged and introduces novel concepts of non-linear metrics and fusion of ROIs. In this context we also introduce a novel way of combining feature vectors for mean-shift grouping. We evaluate our approach on a standarized image database with a HOG detector, which is representative for practical applications. Our results show that the amount of false-positive detections can be reduced by a factor of 4 with a negligable complexity increase. Although introduced and applied to a HOG-based system, our approach can easily be adapted for different detectors.
Object detection systems which operate on large data streams require an efficient scaling with available computation power. We analyze how the use of tile-images can increase the efficiency (i.e. execution speed) of distributed HOG-based object detectors. Furthermore we discuss the challenges of using our developed algorithms in practical large scale scenarios. We show with a structured evaluation that our approach can provide a speed-up of 30-180 % for existing architectures. Due to the its generic formulation it can be applied to a wide range of HOG-based (or similar) algorithms. In this context we also study the effects of applying our method to an existing detector and discuss a scalable strategy for distributing the computation among nodes in a cluster system.
Technical Report
(2016)
This internal report discusses the theoretical and practical aspects of the cluster management framework SimpleHydra, which was developed in order to allow researchers the quick setup of classical small to mid-scale computation clusters while being as lightweight and platform independent as possible. We motivate crucial design choices with a theoretical analysis in the aspect of time and space complexity, furthermore we give a comprehensive introduction regarding the frameworks usage (which includes examples and detailed description of fundamental concepts as well as data structures). In addition to that we illustrate application scenarios with complete source code examples. Furthermore we hope that this document proves valuable not only as a development report but also as a practical manual for SimpleHydra.
We present a novel approach of distributing small-to mid-scale neural networks onto modern parallel architectures. In this context we discuss the induced challenges and possible solutions. We provide a detailed theoretical analysis with respect to space and time complexities and reinforce our computation model with evaluations which show a performance gain over state of the art approaches.
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 %.
This contribution presents a novel approach of utilizing Time-of-Flight (ToF) technology for mid-air hand gesture recognition on mobile devices. ToF sensors are capable of providing depth data at high frame rates independent of illumination making any kind of application possible for in- and outdoor situations. This comes at the cost of precision regarding depth measurements and comparatively low lateral resolution. We present a novel feature generation technique based on a rasterization of the point clouds which
realizes fixed-sized input making Deep Learning approaches applicable using Convolutional Neural Networks. In order to increase precision we introduce several methods to reduce noise and normalize the input to overcome difficulties in scaling. Backed by a large-scale database of about half
a million data samples taken from different individuals our
contribution shows how hand gesture recognition is realiz-
able on commodity tablets in real-time at frame rates of up to 17Hz. A leave-one out cross-validation experiment
demonstrates the feasibility of our approach with classification errors as low as 1,5% achieved persons unknown to the model.
Building upon prior results, we present an alternative approach to efficiently classifying a complex set of 3D hand poses obtained from modern Time-Of-Flight-Sensors (TOF). We demonstrate it is possible to achieve satisfactory results in spite of low resolution and high noise (inflicted by the sensors) and a demanding outdoor environment. We set up a large database of pointclouds in order to train multilayer perceptrons as well as support vector machines to classify the various hand poses. Our goal is to fuse data from multiple TOF sensors, which observe the poses from multiple angles. The presented contribution illustrates that real-time capability can be maintained with such a setup as the used 3D descriptors, the fusion strategy as well as the online confidence measures are computationally efficient.
We present a novel hierarchical approach to multi-class classification which is generic in that it can be applied to different classification models (e.g., support vector machines, perceptrons), and makes no explicit assumptions about the probabilistic structure of the problem as it is usually done in multi-class classification. By adding a cascade of additional classifiers, each of which receives the previous classifier's output in addition to regular input data, the approach harnesses unused information that manifests itself in the form of, e.g., correlations between predicted classes. Using multilayer perceptrons as a classification model, we demonstrate the validity of this approach by testing it on a complex ten-class 3D gesture recognition task.
A light-weight real-time ap- plicable hand gesture recognition system for automotive applications
(2015)
We present a novel approach for improved hand-gesture recognition by a single time-of-flight(ToF) sensor in an automotive environment. As the sensor's lateral resolution is comparatively low, we employ a learning approach comprising multiple processing steps, including PCA-based cropping, the computation of robust point cloud descriptors and training of a Multilayer perceptron (MLP) on a large database of samples. A sophisticated temporal fusion technique boosts the overall robustness of recognition by taking into account data coming from previous classification steps. Overall results are very satisfactory when evaluated on a large benchmark set of ten different hand poses, especially when it comes to generalization on previously unknown persons.
We present a system for efficient dynamic hand gesture recognition based on a single time-of-flight sensor. As opposed to other approaches, we simply rely on depth data to interpret user movement with the hand in mid-air. We set up a large database to train multilayer perceptrons (MLPs) which are subsequently used for classification of static hand poses that define the targeted dynamic gestures. In order to remain robust against noise and to balance the low sensor resolution, PCA is used for data cropping and highly descriptive features, obtainable in real-time, are presented. Our simple yet efficient definition of a dynamic hand gesture shows how strong results are achievable in an automotive environment allowing for interesting and sophisticated applications to be realized.
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 study on 3D based hand pose recognition using a new generation of 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. We investigate the performance of different 3D descriptors, as well as the fusion of two ToF sensor streams. By basing a data fusion strategy on the fact that multilayer perceptrons can produce normalized confidences individually for each class, and similarly by designing information-theoretic online measures for assessing confidences of decisions, we show that appropriately chosen fusion strategies can improve overall performance to a very satisfactory level. Real-time capability is retained as the used 3D descriptors, the fusion strategy as well as the online confidence measures are computationally efficient.
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.
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.