Linear time-derivative trackers

The design of an ideal differentiator is a difficult and a challenging task. In this paper we discuss the properties and the limitations of two different structures of linear differentiation systems. The first time-derivative observer is formulated as a high-gain observer where the observer gain is calculated through a Lyapunov-like dynamical equation. The second one is given in Brunovski form in which the output to be differentiated appears as a control input and the differentiation gain is calculated from the dual Lyapunov equation of the first differentiation observer. A discrete-time version of the second form is given. Finally, illustrative examples are presented to show their strengths and weaknesses.     

Mean shift trackers with cross-bin metrics

Cross-bin metrics have been shown to be more suitable than bin-by-bin metrics for measuring the distance between histograms in various applications. In particular, a visual tracker that minimizes the earth mover's distance (EMD) between the candidate and reference feature histograms has recently been proposed. This tracker was shown to be more robust than the Mean Shift tracker, which employs a bin-by-bin metric. In each frame, the former tracker iteratively shifts the candidate location by one pixel in the direction opposite to the EMD's gradient until no improvement is made. This optimization process involves the clustering of the candidate feature density in feature space, as well as the computation of the EMD between the candidate and reference feature histograms after each shift of the candidate location. In this paper, alternative trackers that employ cross-bin metrics as well, but that are based on Mean Shift (MS) iterations, are derived. The proposed trackers are simpler and faster due to 1) the use of MS-based optimization, which is not restricted to single pixel shifts, 2) abstention from any clustering of feature densities, and 3) abstention from EMD computations in multidimensional spaces.     

Learning-based dynamic coupling of discrete and continuous trackers

We present a data-driven dynamic coupling between discrete and continuous methods for tracking objects of high dofs, which overcomes the limitations of previous techniques. In our approach, two trackers work in parallel, and the coupling between them is based on the tracking error. We use a model-based continuous method to achieve accurate results and, in cases of failure, we re-initialize the model using our discrete tracker. This method maintains the accuracy of a more tightly coupled system, while increasing its efficiency. At any given frame, our discrete tracker uses the current and several previous frames to search into a database for the best matching solution. For improved robustness, object configuration sequences, rather than single configurations, are stored in the database. We apply our framework to the problem of 3D hand tracking from image sequences and the discrimination between fingerspelling and continuous signs in American Sign Language.     

Dynamic response of electromagnetic spatial displacement trackers

Overall system latency--the elapsed time from input human motion until the immediate consequences of that input are available in the display--is one of the most frequently cited shortcoming of current virtual environment (VE) technology. Given that spatial displacement trackers are employed to monitor head and hand position and orientation in many VE applications, the dynamic response intrinsic to these devices is an unavoidable contributor to overall system latency. In this paper, we describe a testbed and method for measurement of tracker dynamic response that use a motorized rotary swing arm to sinusoidally displace the VE sensor at a number of frequencies spanning the bandwidth of volitional human movement. During the tests, actual swing arm angle and VE sensor reports are collected and time stamped. By calibrating the time stamping technique, the tracker's internal transduction and processing time are separated from data transfer and host computer software execution latencies. We have used this test-bed to examine several VE sensors--most recently to compare latency, gain, and noise characteristics of two commercially available electromagnetic trackers: Ascension Technology Corp.'s Flock of Birds(TM) and Polhemus Inc.'s Fastrak(TM).     

Forensic analysis for IoT fitness trackers and its application

Peer-to-Peer Networking and Applications - A fitness tracker monitors our daily activity by measuring distance walked (or run), calorie consumption, heartbeat and quality of sleep. Although...     

Automated,highly-accurate,bug assignment using machine learning and tossing graphs

Empirical studies indicate that automating the bug assignment process has the potential to significantly reduce software evolution effort and costs. Prior work has used machine learning techniques to automate bug assignment but has employed a narrow band of tools which can be ineffective in large, long-lived software projects. To redress this situation, in this paper we employ a comprehensive set of machine learning tools and a probabilistic graph-based model (bug tossing graphs) that lead to highly-accurate predictions, and lay the foundation for the next generation of machine learning-based bug assignment. Our work is the first to examine the impact of multiple machine learning dimensions (classifiers, attributes, and training history) along with bug tossing graphs on prediction accuracy in bug assignment. We validate our approach on Mozilla and Eclipse, covering 856,259 bug reports and 21 cumulative years of development. We demonstrate that our techniques can achieve up to 86.09% prediction accuracy in bug assignment and significantly reduce tossing path lengths. We show that for our data sets the Naïve Bayes classifier coupled with product–component features, tossing graphs and incremental learning performs best. Next, we perform an ablative analysis by unilaterally varying classifiers, features, and learning model to show their relative importance of on bug assignment accuracy. Finally, we propose optimization techniques that achieve high prediction accuracy while reducing training and prediction time.     

Autocalibration-based partioning relationship and parallax relation for head-mounted eye trackers

This paper presents new methods to calibrate a head-mounted Eye Tracker (ET) automatically, as well as a new way to obtain an estimated point of regard (POR), taking account of the parallax. Calibration is performed in real time; it is easy for the user who just needs to look at one calibration pattern for a few seconds before starting. This method provides a very important couple of points which helps to use a local relationship to compute the POR instead of a global one. This approach significantly improves the precision of the points of regard when the scene camera is mounted with a short focal lens. An estimation of POR when the user looks somewhere outside the calibration distance is also proposed. This estimation is based on an ET modelling such as a stereovision system, to take account of the parallax effect. The aim of this study is to simplify the use of ET techniques for “non-initiated” people, especially here learner drivers.     

A public unified bug dataset for java and its assessment regarding metrics and bug prediction

Software Quality Journal - Bug datasets have been created and used by many researchers to build and validate novel bug prediction models. In this work, our aim is to collect existing public source...     

Accurate and stable alignment of virtual and real spaces using consumer-grade trackers

Virtual Reality - The world space defined by a traditional virtual reality application is generally regarded as separate from the real-world space in which users actually exist. In this paper, we...     

It's really not a rendering bug, you see

Most rendering algorithms deliberately employ approximations and other shortcuts for efficiency. These economies-not coding errors-produce characteristic image artifacts. This article classifies the best-known varieties. We wrote this article in the hope of saving programmers a lot of time debugging something that is really not a bug, but instead a limitation of the algorithm. Our taxonomy is not exhaustive, but it does include many common rendering problems that we have encountered over the years. Most of these limitations can be circumvented with the proposed solutions. In reality, there are more workarounds than true solutions. This imbalance warrants more research into proper solutions to these and similar problems     

Thermo-visual feature fusion for object tracking using multiple spatiogram trackers

In this paper, we propose a framework that can efficiently combine features for robust tracking based on fusing the outputs of multiple spatiogram trackers. This is achieved without the exponential increase in storage and processing that other multimodal tracking approaches suffer from. The framework allows the features to be split arbitrarily between the trackers, as well as providing the flexibility to add, remove or dynamically weight features. We derive a mean-shift type algorithm for the framework that allows efficient object tracking with very low computational overhead. We especially target the fusion of thermal infrared and visible spectrum features as the most useful features for automated surveillance applications. Results are shown on multimodal video sequences clearly illustrating the benefits of combining multiple features using our framework.     

On the construction of static stabilizers and static output trackers for dynamically linearizable systems,related results and applications

It is shown that the property of dynamic linearizability, to be understood as linearizability by means of the dynamic extension algorithm, implies the existence of static, possibly time varying, control laws yielding asymptotic output tracking with arbitrary speed of convergence and asymptotic stabilization with a computable bound on the region of attraction. Similar results hold for systems which are only Input/Output linearizable by means of dynamic state feedback, provided that the inverse dynamics possess certain stability properties. Applications of these results to the problem of regional stabilization of a VTOL aircraft is considered, together with the tracking problem for a class of flexible joints robots. Moreover, a novel parameterization for flexible joint robots is also proposed.     

Health empowerment through activity trackers: An empirical smart wristband study

The increasing popularity of activity trackers has shown a remarkable shift in human computer interaction; individuals seem willing to wear a device that constantly tracks health related metrics such as movement, exercise, sleep, and calorie burn. Using the insights derived from their activity trackers, individuals are expected to be more empowered to set and stick to personal health goals. Whereas the outcome of using activity trackers is of great importance to both individuals and society at large, there are no empirical studies substantiating this presumption. This study aims to contribute to filling this research gap. Making use of self-regulation theory as theoretical framework, we developed a model that proposes six system specific elements (attractiveness, monitoring, feedback, privacy protection, readability, and gamification) as determinants of health empowerment, and thereof health commitment. Using survey data collected from individuals wearing smart wristbands (N = 210), the model was validated. Overall, the results provide strong support for the health empowering capabilities of smart wristbands. The paper concludes with implications for theory and practice, and some suggestions for future research.     

Lyapunov design of global state and output feedback trackers for non-holonomic control systems

In this paper, new results are obtained for the global tracking of a class of non-holonomic dynamic systems via state and output feedback. The tracking controllers are systematically constructed on the basis of a recursive technique and a full exploitation of the system structure. When disturbances occur in a non-holonomic chained system, it is shown how to modify the controller design procedure to yield robust tracking control laws. The proposed method is demonstrated and discussed by means of a benchmark non-holonomic knife-edge mechanical system.