1,721,214 research outputs found
Intelligent Collision Avoidance, Control and Guidance
No full textThis paper reviews a variety of advanced signal processing algorithms that have been developed at the University of Southampton as part of the Prometheus (Programme for European traffic flow with highest efficiency and unprecedented safety) programme to achieve an intelligent driver warning system (IDWS). The IDWS includes the detection of road edges, lanes, obstacles and their tracking and identification, estimates of time to collision, and behavioural modelling of drivers for a variety of scenarios. The underlying algorithms are briefly discussed in support of the IDWS
Attentive visual tracking and trajectory estimation for dynamic scene segmentation
No full textIntelligent Co-Pilot Systems (ICPS) offer the next challenge to vehicle-highway automation. The key to ICPSs is the detection of moving objects (other vehicles) from the moving observer using a visual sensor. The aim of the work presented in this thesis was to design and implement a feature detection and tracking strategy that is capable of tracking image features independently, in parallel, and in real-time and to cluster/segment features utilising the inherent temporal information contained within feature trajectories. Most images contain areas that are of little or no interest to vision tasks. An attentive, data-driven, approach to feature detection and tracking is proposed which aims to increase the efficiency of feature detection and tracking by focusing attention onto relevant regions of the image likely to contain scene structure. This attentive algorithm lends itself naturally to parallelisation and results from a parallel implementation are presented. A scene may be segmented into independently moving objects based on the assumption that features belonging to the same object will move in an identical way in three dimensions (this assumes objects are rigid). A model for scene segmentation is proposed that uses information contained within feature trajectories to cluster, or group, features into independently moving objects. This information includes: image-plane position, time-to-collision of a feature with the image-plane, and the type of motion observed. The Multiple Model Adaptive Estimator (MMAE) algorithm is extended to cope with constituent filters with different states (MMAE2) in an attempt to accurately estimate the time-to-collision of a feature and provide a reliable idea of the type of motion observed (in the form of a model belief measure). Finally, poor state initialisation is identified as a likely prime cause for poor Extended Kalman Filter (EKF) performance (and hence poor MMAE2 performance) when using high order models. The idea of the neurofuzzy initialised EKF (NF-EKF) is introduced which attempts to reduce the time for an EKF to converge by improving the accuracy of the EKF's initial state estimates
Multiple Model Filtering for Time-to-Collision Estimation and Segmentation
No full textThis paper addresses the problem of time-to-collision/recession-rate estimation of tracked two-dimensional image features over a long image sequence using the Multiple Model Adaptive Estimator (MMAE). Extended Kalman filters are constructed assuming that an image feature moves on a constant plane with respect to an observer, and simulations are then presented. These simulation results show that a filter based on a single motion model is not appropriate when trying to estimate the time-to-collision of a tracked feature point in a typical driving scenario, due to either undermodelling or overmodelling of the expected feature motion. A Multiple Model Filter algorithm (the MMAE2) is then proposed and investigated to overcome these problems and to help with scene segmentation. This algorithm was found to be unreliable when tested on simple artificially generated feature trajectories and could not discriminate reliably between models of different orders. Finally, a modified version of the MMAE2 using empirically generated estimates of the residual's covariance, instead of the theoretical covariance traditionally used, is proposed. The modified algorithm (MMAE2) was tested and was found to discriminate reliably between models and therefore produce accurate estimates of recession-rate
Parallel Attentive Visual Tracking
No full textThe research reported here addresses the problem of detecting and tracking independently moving objects from a moving observer in real time, using corners as object tokens. Corners are detected using the Harris corner detector and local image-plane constraints are employed to solve the correspondence problem. The approach relaxes the restrictive static-world assumption conventionally made, and is therefore capable of tracking independently moving and deformable objects. Tracking is performed without the use of any 3-dimensional motion model. The technique is novel in that, unlike traditional feature tracking algorithms where feature detection and tracking is carried out over the entire image-plane, here it is restricted to those areas most likely to contain meaningful image structure. Two distinct types of instantiation regions are identified, these being the focus-of-expansion region and border regions of the image-plane. The size and location of these regions are defined from a combination of odometry information and a limited knowledge of the operating scenario. The algorithms developed have been tested on real image sequences taken from typical driving scenarios. Implementation of the algorithm using T800 Transputers has shown that near-linear speedups are achievable, and that real-time operation is possible (half-video rate has been achieved using 30 processing elements)
Parallel Visual Tracking
No full textThe research reported here addresses the problem of tracking independently moving objects from a moving observer in real time, using corners as object tokens (Harris and Stephens, 1988). A novel algorithm is developed to solve the local correspondence/tracking problem. This algorithm relaxes the restrictive static-world assumption traditionally made, and is therefore capable of tracking independently moving objects. A parallel (Transputer) architecture is described on which to implement the algorithm. Implementation of the algorithm using T800 transputers has shown that near-linear speedups are achievable, and that real time operation is possible (half-video rate has been achieved using 30 processing elements)
Attentive Visual Tracking
No full textThe research reported here addresses the problem of detecting and tracking independently moving objects from a moving observer in real time, using corners as object tokens. Local image-plane constraints are employed to solve the correspondence problem. The approach relaxes the restrictive static-world assumption conventionally made, and is therefore capable of tracking independently moving and deformable objects. Tracking is performed without the use of any 3-dimensional motion model. The technique is novel in that, unlike traditional feature tracking algorithms where feature detection and tracking is carried out over the entire image-plane, here it is restricted to those areas most likely to contain meaningful image structure. Feature instantiation regions are defined from a combination of odometry information and a limited knowledge of the operating scenario. The algorithms developed have been tested on real image sequences taken from typical driving scenarios. Preliminary experiments on a parallel (Transputer) architecture indicate that real-time operation is achievable
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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