1,721,095 research outputs found
Lane Mark Detection with Pre-Aligned Spatial-Temporal Attention
No full textLane mark detection plays an important role in autonomous driving under structural environments. Many deep learning-based lane mark detection methods have been put forward in recent years. However, most of current methods limit their solutions within one single image and do not make use of the de facto successive image input during the driving scene, which may lead to inferior performance in some challenging scenarios such as occlusion, shadows, and lane mark degradation. To address the issue, we propose a novel lane mark detection network which takes pre-aligned multiple successive frames as inputs to produce more stable predictions. A Spatial-Temporal Attention Module (STAM) is designed in the network to adaptively aggregate the feature information of history frames to the current frame. Various structure of the STAM is also studied to ensure the best performance. Experiments on Tusimple and ApolloScape datasets show that our method can effectively improve lane mark detection and achieve state-of-the-art performance
Robust real-time lane detection based on lane mark segment features and general a priori knowledge
No full textInternational audienceLane detection plays an important role in vision based intelligent vehicle systems. A new lane detection method based on lane mark segment features and general a priori knowledge is proposed in this paper. Instead of detecting each feature point separately from limited local view, a lane mark segment detection method is designed for detecting each lane mark segment on the whole. Some a priori knowledge which is quite general for real traffic scenarios is used in the lane mark segment detection method as well as in the part of model fitting. The tracking process which ensures detection stability and robustness is carried out in the framework of particle filtering. The performance of the proposed method has been demonstrated based on the test on thousands of road images; these road images include scenarios with many kinds of uncertainties such as variation of lighting condition, existence of leading vehicles etc. The research direction for further improvements is also discusse
Intelligent road lane mark extraction using a Mobile Mapping System
Full text link102 p.During the last years, road landmark in- ventory has raised increasing interest in different areas: the maintenance of transport infrastructures, road 3d modelling, GIS applications, etc. The lane mark detection is posed as a two-class classification problem over a highly class imbalanced dataset. To cope with this imbalance we have applied Active Learning approaches. This Thesis has been divided into two main com- putational parts. In the first part, we have evaluated different Machine Learning approaches using panoramic images, obtained from image sensor, such as Random Forest (RF) and ensembles of Extreme Learning Machines (V-ELM), obtaining satisfactory results in the detection of road continuous lane marks. In the second part of the Thesis, we have applied a Random Forest algorithm to a LiDAR point cloud, obtaining a georeferenced road horizontal signs classification. We have not only identified continuous lines, but also, we have been able to identify every horizontal lane mark detected by the LiDAR sensor
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
Comparative study of the telepresence control of the humanoid robot with the objective of following a white lane mark in an office environment with live video feedback.
No full text<p>Comparative study of the telepresence control of the humanoid robot with the objective of following a white lane mark in an office environment with live video feedback.</p
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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