1,721,057 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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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Vision based, Multi-cue Driver Models for Intelligent Vehicles
Get PDFThis dissertation seeks to enable intelligent vehicles to see, to predict intentions, to understand and to model the state of driver.We developed a state of the art vision based non-contact gaze estimation framework by carefully designing submodules which will build up to achieve continuous and robust estimation. Key modules in this system include, face detection using deep convolutional neural networks, landmark estimation from cascaded regression models, head pose from geometrical correspondence mapping from 2-D points in the image plane to 3-D points in the head model, horizontal gaze surrogate based on geometrical formulation of the eye ball and iris position, vertical gaze surrogate based on openness of the upper eye lids and appearance descriptor, and finally, a 9-class gaze zone estimation from naturalistic driving data driven random forest algorithm. We developed a framework to model driver's gaze behavior by representing the scanpath over a time period using glance durations and transition frequencies. As a use case, we explore the driver's scanpath patterns during maneuvers executed in freeway driving, namely, left lane change maneuver, right lane change maneuver and lane keep. It is shown that condensing temporal scanpath into glance durations and glance transition frequencies leads to recurring patterns based on driver activities. Furthermore, modeling these patterns show predictive powers in maneuver detection up to a few seconds a priori and show a promise for developing gaze guidance during take over requests in highly automated vehicles.We introduce a framework to model the spatio-temporal movements of head, eyes and hands given naturalistic driving data of looking-in at the driver for any events or tasks performed of interest. As a use case, we explore the temporal coordination of the modalities on data of drivers executing maneuvers at stop-controlled intersections; the maneuvers executed are go straight, turn left and turn right. In sequentially increasing time windows, by training classifiers which have the ability to provide discriminative quality of its input variable, the experimental study at intersections shows which type of, when and how long distinguishable preparatory movements occur in the range of a few milliseconds to a few seconds.We introduce one part of the Vision for Intelligent Vehicles and Applications (VIVA) challenge, namely, the VIVA-face challenge. VIVA is a platform designed to share naturalistic driving data with the community in order to: present issues and challenges in vision from real-world driving conditions, benchmark existing vision approaches using proper metrics and progress the development of future vision algorithms. With a special focus on challenges from looking inside at the driver’s face, we provide information on how the data is acquired and annotated, and how methods are benchmarked, compared and shared on leaderboards.Finally, we propose de-identification filters for protecting the privacy of drivers while preserving sufficient details to infer driver behavior, such as the gaze direction, in naturalistic driving videos. We implement and compare de-identification filters, which are made up of a combination of preserving eye regions and distorting the background, to show promising results. With such filters, researchers may be more inclined to publicly share deidentified naturalistic driving data. The research community can then tremendously benefit from large amounts of naturalistic driving data and focus on the analysis of human factors in the design and evaluation of intelligent vehicles
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Predicting Pedestrian Crossing Intention
Get PDFAutonomous vehicles face significant challenges in understanding pedestrian behavior, particularly in urban environments. The system must recognize pedestrians’ intentions and anticipate their actions to achieve intelligent driving. This paper focuses on predicting pedestrian crossings, aiming to enable oncoming vehicles to react in a timely manner. We investigate the effectiveness of various input modalities for pedestrian crossing prediction, including human poses, bounding boxes and ego vehicle speed features. We propose a novel lightweight architec- ture based on LSTM and attention to accurately identifying crossing pedestrians. Our methods evaluated on two widely used public datasets for pedestrian behavior, PIE and JAAD datasets,and our algorithm achieved a state-of-the-art performance in both datasets
Traffic sign detection using computer vision: Explorations for a driver support system
No full textThis report is a master thesis in Vision, Graphics, and Interactive Systems. It details the work done during two semesters abroad at UC San Diego. The work has been research oriented, so the report is structured with 5 separate chapters instead of a linear product development flow.The work has primarily been on US traffic sign detection, but includes a chapter on pedestrian detection as well. A comprehensive survey of traffic sign detection systems has been made and it shows a lack of work with US signs and a lack of public databases for those. Thus, a publicly available dataset with nearly 8000 annotated signs has been created. The dataset is unique, not only because it contains US signs, but also because it include videos. This report also details investigations of using synthetic training data for traffic sign detectors, but concludes that synthetic images are no match for real-world training images. A purely model based detection system based solely on shapes is also presented as a building block for a full detection system. Finally, a two-stage pedestrian detection system has been developed and documented. The system extends a prevous system and produces better detection with fewer false positives.The work has resulted in the submission of four papers, one to ITS Transactions, one to ICPR and two to ITSC. A the time of writing the journal paper is in the second review stage
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Learning, Classification and Prediction of Maneuvers of Surround Vehicles at Intersections using LSTMs
Get PDFBehavior analysis of vehicles surrounding the ego-vehicle is an essential component in safe and pleasant autonomous driving. This study develops a framework for activity classification of observed on-road vehicles using 3D trajectory cues and a Long Short Term Memory (LSTM) model. As a case study, we aim to classify maneuvers of surrounding vehicles at four way intersections. A variety of sensros including stereo cameras, LIDAR, GPS, and IMU measurements are used to extract ego-motion compensated surround trajectories from five different datasets. The impact of different prediction label space choices, feature space input, noisy/missing trajectory data, and LSTM model architectures are analyzed, presenting the strengths and limitations of the proposed approach
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Intelligent Human-in-the-Loop Vehicular Automation with Real-Time Vision Models
Get PDFModern vehicular automation technology is enabled by complex interactive systems composed of discrete blocks that accomplish specific tasks. These blocks are responsible for perceiving the environment surrounding the vehicle, predicting the future states and intents of other agents in its vicinity, planning the vehicle's path with an eventual goal in mind, and finally, providing control signals and actuations that achieve this goal. While such systems are widely adopted, they fail to consider a key component of the driving process - the human occupants inside the vehicle. This dissertation proposes that to ensure a safe, smooth, and enjoyable travel experience, it is essential for the human driver and vehicular automation to be aware of each other's states and impending failures, i.e., a relationship built on earned trust rather than blind faith. To codify this requirement, we employ the Looking-In & Looking-Out (LILO) approach, where systems simultaneously model the inside and outside of vehicles. In addition to the set of "looking-out" tasks that are used in conventional automation, we propose a parallel set of "looking-in" tasks that model and analyze the interior of the vehicle. The outputs generated by both these parallel systems can then be integrated to provide useful controls based on a more complete understanding of the circumstances and the environment.This dissertation presents the models and algorithms we have developed to accomplish the individual tasks that make up the system described above. This includes our research on real-time models for tasks such as object detection, multi-object tracking, and maneuver/trajectory prediction. We also introduce new approaches for the relatively under-explored "looking-in" tasks - which we hope will inspire many such studies in the future. Some of our contributions in this space include real-time models to analyze different aspects of the driver's state, data augmentation and automatic labelling schemes for tasks with limited data, and solutions to other common hindrances. Finally, we provide a concrete example of how the LILO framework can be used in practice - where the driver’s state estimated by "looking-in" is used in conjunction with metrics computed by "looking-out" to initiate safer and smoother control transitions
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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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