1,720,976 research outputs found
Motion estimation by vision for mobile mapping with motorcycle
No full textIn this paper we present a vision algorithm to estimate the angular velocity of a motorcycle. This estimate, integrated with the measurements provided by other sensors such as a speedometer allows for a complete reconstruction of the trajectory followed by a motorcycle. The proposed scheme is, then, a valid alternative to the use of costly inertial platform to compensate for missing GPS data in order to geo-register information gathered by on-board sensors
Model Based GPS/INS Integration for High Accuracy Land Vehicle Applications: Calibration of a Swarm of MEMS Sensors
No full textWe consider the problem of reconstructing the trajectory of a mobile mapping system based on a midsize van. Mobile mapping requires, of course, high accuracy. Usually this is achieved by resorting to costly GPS/INS integrated systems. The INS, in particular, must guarantee high performance when the GPS signal is occluded. This paper concerns the possibility of using, in alternative, a swarm of low cost MEMS accelerometers mounted in random positions and orientations. In order to be able to reconstruct the trajectory, the relative position and orientation of each accelerometer should be known. Here, we propose a method for an automatic calibration of the cloud of MEMS sensors and an algorithm for trajectory reconstruction by GPS and MEMS accelerometers integratio
A Bayesian Approach to Road Image Segmentation
No full textIt is well known that Mobile Mapping Systems (MMS) offer countless possibilities for data collection and field inventory to government agencies, public utilities, and engineering and architectural firms. Despite major progress has been made over last years in terms of sensor resolution, data rate, positioning accuracy and operational flexibility, the detection of road edges on collected the digital images is performed not yet automatically, what in some sense limits the productivity of these surveying systems.
In this paper an automatic procedure for digital image segmentation will be presented, whose main goal concerns with the detection of road edges from the image sequence collected by an MMS. At the present, the application of developed method is limited to black and white digital images, acquired by a pair of mid-resolution CCD cameras (720x578 pixels), mounted on the windscreen of the vehicle (car or van). In the initial stage, each b/w image is applied the Canny edge detector and the Hough transform, in order to obtain a first approximate estimate of road edges. Then, the detection system is made more robust against any environmental condition through further image processing based on image texture analysis and the application of an extended Kalman filter. Average and standard deviation values for brightness and texture are calculated for each digital image and a Bayesian classification is performed in order to define probability maps for each component (sky, road, background) of the viewed scene. In turn, the extended Kalman filter allows to compute at each iteration the predicted positions of road edges on the next image. From these data a “virtual” image is built and then a-priori probability map is computed, by which the road segmentation can be more efficiently driven
Building a Normality Space of Events - A PCA Approach to Event Detection
No full textThe detection of events in video streams is a central task in the automatic vision paradigm, and spans het- erogeneous fields of application from the surveillance of the environment, to the analysis of scientific data. Actually, although well captured by intuition, the definition itself of event is somewhat hazy and depending on the specific application of interest. In this work, the approach to the problem of event detection is different in nature. Instead of defining the event and searching for it within the data, a normality space of the scene is built from a chosen learning sequence The event detection algorithm works by projecting any newly acquired image onto the normality space so as to calculate a distance from it that represents the innovation of the new frame, and defines the metric for triggering an event alert
Integrating dynamic and probabilistic shape information for data association and tracking
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METHOD AND SYSTEM FOR MONITORING AN ENVIRONMENT
No full textThe invention relates to a method for monitoring an environment through a plurality of sensors, wherein a control system receives information from one or more sensors of said plurality and uses said information in order to monitor said environment. The method comprises a setup stage wherein an operator creates a model of an environment by defining a plurality of cells corresponding to areas of said environment, and then creates cell/sensor relationships by defining for each sensor at least one possible position which is associated with at least one cell. For each position the sensor is assigned by the operator a monitoring judgment for the associated cell. The method also comprises an operational stage wherein the control system, in order to perform a surveillance function, finds those sensors which can be used for carrying out the requested surveillance function and controls them based on the monitoring judgments and the cell/sensor relationships
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