Browsing by Author "Torres Torriti, M."

Now showing 1 - 4 of 4
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    A comparison of Bayesian prediction techniques for mobile robot trajectory tracking
    (CAMBRIDGE UNIV PRESS, 2008) Peralta Cabezas, J. L.; Torres Torriti, M.; Guarini Hermann, M.
    This paper presents a performance comparison of different estimation and prediction techniques applied to the problem of tracking multiple robots. The main performance criteria are the magnitude of the estimation or prediction error, the computational effort and the robustness of each method to non-Gaussian noise. Among the different techniques compared are the well-known Kalman filters and their different variants (e.g. extended and unscented), and the more recent techniques relying on Sequential Monte Carlo Sampling methods, such as particle filters and Gaussian Mixture Sigma Point Particle Filter.
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    Crowded pedestrian counting at bus stops from perspective transformations of foreground areas
    (INST ENGINEERING TECHNOLOGY-IET, 2012) Garcia Bunster, G.; Torres Torriti, M.; Oberli, C.
    Automated bus fleet scheduling and dispatch require an accurate measurements of current passenger demand. This study presents an effective holistic approach for estimating the number of people waiting at regular open bus stops by means of image processing. This is a non-trivial problem because of several varying conditions that complicate the detection process, such as illumination, crowdedness and different people poses, to name a few. The proposed method estimates the pedestrian count using measurements of foreground areas corrected by perspective. Four approaches are evaluated to find the best mapping between the area measurements and the people count. These mappings include two parametric (standard linear regression model, linear discriminant analysis) and two non-parametric (probabilistic neural network, k-nearest neighbours) approaches. This study also evaluates the performance of the algorithm when thermal and panoramic catadioptric cameras are used instead of standard perspective colour cameras. The proposed method is shown to yield better pedestrian count estimates than those obtained using milestone detectors, and requires model fitting procedures than can be easily implemented without requiring very large datasets for proper classifier training. The approach can also be employed to count people in other public spaces, such as buildings and crosswalks.
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    Face salient points and eyes tracking for robust drowsiness detection
    (CAMBRIDGE UNIV PRESS, 2012) Jimenez Pinto, J.; Torres Torriti, M.
    Measuring a driver's level of attention and drowsiness is fundamental to reducing the number of traffic accidents that often involve bus and truck drivers, who must work for long periods of time under monotonous road conditions. Determining a driver's state of alert in a noninvasive way can be achieved using computer vision techniques. However, two main difficulties must be solved in order to measure drowsiness in a robust way: first, detecting the driver's face location despite variations in pose or illumination; secondly, recognizing the driver's facial cues, such as blinks, yawns, and eyebrow rising. To overcome these challenges, our approach combines the well-known Viola-Jones face detector with the motion analysis of Shi-Tomasi salient features within the face. The location of the eyes and blinking is important to refine the tracking of the driver's head and compute the so-called PERCLOS, which is the percentage of time the eyes are closed over a given time interval. The latter cue is essential for noninvasive driver's alert state estimation as it has a high correlation with drowsiness. To further improve the location of the eyes under different conditions of illumination, the proposed method takes advantage of the high reflectivity of the retina to near infrared illumination employing a camera with an 850 nm wavelength filter. The paper shows that motion analysis of the salient points, in particular cluster mass centers and spatial distributions, yields better head tracking results compared to the state-of-the-art and provides measures of the driver's alert state.
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    Mobile robot localization using the Hausdorff distance
    (CAMBRIDGE UNIV PRESS, 2008) Donoso Aguirre, F.; Bustos Salas, J. P.; Torres Torriti, M.; Guesalaga, A.
    This paper presents a novel method for localization of mobile robots in structured environments. The estimation of the position and orientation of the robot relies on the minimisation of the partial Hausdorff distance between ladar range measurements and a floor plan image of the building. The approach is employed in combination with an extended Kalman filter to obtain accurate estimates of the robot's position, heading and velocity. Good estimates of these variables were obtained during tests performed using a differential drive robot, thus demonstrating that the approach provides an accurate, reliable and computationally feasible alternative for indoor robot localization and autonomous navigation.