scholarly journals A Novel Approach to Measuring the Useful Field of View in Simulated Real-World Environments Using Gaze Contingent Displays: The GC-UFOV.

2015 ◽  
Vol 15 (12) ◽  
pp. 878 ◽  
Author(s):  
Ryan Ringer ◽  
Zachary Throneburg ◽  
Tera Walton ◽  
Greg Erickson ◽  
Allison Coy ◽  
...  
Keyword(s):  
Real World ◽  
Field Of View ◽  
Useful Field Of View ◽  
Novel Approach

Age Differences in the Useful Field of View during Real-World Driving

2005 ◽  
Vol 49 (2) ◽  
pp. 182-185 ◽  
Author(s):  
Frank Schieber ◽  
Jess Gilland
Keyword(s):  
Target Detection ◽  
Age Differences ◽  
Real World ◽  
Field Of View ◽  
Peripheral Target ◽  
Car Following ◽  
Useful Field Of View ◽  
Target Eccentricity ◽  
Age Related ◽  
Age Related Changes

Age differences in the useful field of view (UFOV) were assessed during real-world driving using a newly developed car-following protocol. Nineteen young (mean age = 23) and 19 older (mean age = 73) drivers were examined. Peripheral target detection performance declined significantly with age and target eccentricity. However, consistent with several recent studies, no age by target eccentricity interaction was observed. These findings contribute to the validation of the UFOV construct and provide a foundation for better understanding age-related changes in visual attention in the real-world driving domain.

scholarly journals Developing a New Measure of the Useful Field of View for Use in Dynamic Real-World Scene Viewing

2012 ◽  
Vol 12 (9) ◽  
pp. 564-564
Author(s):  
L. Loschky ◽  
R. Ringer ◽  
A. Larson ◽  
G. Hughes ◽  
K. Dean ◽  
...  
Keyword(s):  
Real World ◽  
Field Of View ◽  
Useful Field Of View ◽  
Scene Viewing

scholarly journals Extrinsic Camera Calibration with Line-Laser Projection

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1091
Author(s):  
Izaak Van Crombrugge ◽  
Rudi Penne ◽  
Steve Vanlanduit
Keyword(s):  
Camera Calibration ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Bundle Adjustment ◽  
Field Of View ◽  
Extrinsic Calibration ◽  
Practical Procedure ◽  
Partial Overlap

Knowledge of precise camera poses is vital for multi-camera setups. Camera intrinsics can be obtained for each camera separately in lab conditions. For fixed multi-camera setups, the extrinsic calibration can only be done in situ. Usually, some markers are used, like checkerboards, requiring some level of overlap between cameras. In this work, we propose a method for cases with little or no overlap. Laser lines are projected on a plane (e.g., floor or wall) using a laser line projector. The pose of the plane and cameras is then optimized using bundle adjustment to match the lines seen by the cameras. To find the extrinsic calibration, only a partial overlap between the laser lines and the field of view of the cameras is needed. Real-world experiments were conducted both with and without overlapping fields of view, resulting in rotation errors below 0.5°. We show that the accuracy is comparable to other state-of-the-art methods while offering a more practical procedure. The method can also be used in large-scale applications and can be fully automated.

scholarly journals Movement Around Real and Virtual Cluttered Environments

2005 ◽  
Vol 14 (5) ◽  
pp. 580-596 ◽  
Author(s):  
Simon Lessels ◽  
Roy A. Ruddle
Keyword(s):  
Real World ◽  
Search Task ◽  
Scale Space ◽  
Visual Scene ◽  
Field Of View ◽  
Small Scale ◽  
High Fidelity ◽  
Cluttered Environments ◽  
The Real ◽  
Desktop Virtual Environment

Two experiments investigated participants' ability to search for targets in a cluttered small-scale space. The first experiment was conducted in the real world with two field of view conditions (full vs. restricted), and participants found the task trivial to perform in both. The second experiment used the same search task but was conducted in a desktop virtual environment (VE), and investigated two movement interfaces and two visual scene conditions. Participants restricted to forward only movement performed the search task quicker and more efficiently (visiting fewer targets) than those who used an interface that allowed more flexible movement (forward, backward, left, right, and diagonal). Also, participants using a high fidelity visual scene performed the task significantly quicker and more efficiently than those who used a low fidelity scene. The performance differences among all the conditions decreased with practice, but the performance of the best VE group approached that of the real-world participants. These results indicate the importance of using high fidelity scenes in VEs, and suggest that the use of a simple control system is sufficient for maintaining one's spatial orientation during searching.

scholarly journals Silica-encapsulated DNA tracers for measuring aerosol distribution dynamics in real-world settings

2021 ◽  
Author(s):  
Anne M Luescher ◽  
Julian Koch ◽  
Wendelin J Stark ◽  
Robert N Grass
Keyword(s):  
Real World ◽  
Complex Dynamics ◽  
Quantitative Polymerase Chain Reaction ◽  
Cost Effective ◽  
Confined Space ◽  
Aerosol Dynamics ◽  
Novel Approach ◽  
Aerosol Distribution ◽  
Wide Range ◽  
Series Of Experiments

Aerosolized particles play a significant role in human health and environmental risk management. The global importance of aerosol-related hazards, such as the circulation of pathogens and high levels of air pollutants, have led to a surging demand for suitable surrogate tracers to investigate the complex dynamics of airborne particles in real-world scenarios. In this study, we propose a novel approach using silica particles with encapsulated DNA (SPED) as a tracing agent for measuring aerosol distribution indoors. In a series of experiments with a portable setup, SPED were successfully aerosolized, re-captured and quantified using quantitative polymerase chain reaction (qPCR). Position-dependency and ventilation effects within a confined space could be shown in a quantitative fashion achieving detection limits below 0.1 ng particles per m3 of sampled air. In conclusion, SPED show promise for a flexible, cost-effective and low-impact characterization of aerosol dynamics in a wide range of settings.

scholarly journals Optimising Daily Fantasy Sports Teams with Artificial Intelligence

2020 ◽  
Vol 19 (2) ◽  
pp. 21-35
Author(s):  
Ryan Beal ◽  
Timothy J. Norman ◽  
Sarvapali D. Ramchurn
Keyword(s):  
Real World ◽  
National Football League ◽  
Mixed Integer ◽  
Data Sets ◽  
Fantasy Sports ◽  
Real World Data ◽  
Sports Teams ◽  
Novel Approach ◽  
Four Seasons ◽  
Daily Fantasy Sports

AbstractThis paper outlines a novel approach to optimising teams for Daily Fantasy Sports (DFS) contests. To this end, we propose a number of new models and algorithms to solve the team formation problems posed by DFS. Specifically, we focus on the National Football League (NFL) and predict the performance of real-world players to form the optimal fantasy team using mixed-integer programming. We test our solutions using real-world data-sets from across four seasons (2014-2017). We highlight the advantage that can be gained from using our machine-based methods and show that our solutions outperform existing benchmarks, turning a profit in up to 81.3% of DFS game-weeks over a season.

scholarly journals Identifying and Labeling Potentially Risky Driving: A Multistage Process Using Real-World Driving Data

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Charles Marks ◽  
Arash Jahangiri ◽  
Sahar Ghanipoor Machiani
Keyword(s):  
Real World ◽  
Traffic Accidents ◽  
Network Models ◽  
Multistage Process ◽  
Neural Network Models ◽  
Risky Driving ◽  
Vehicle Accidents ◽  
Driving Behaviors ◽  
Novel Approach ◽  
Risky Driving Behaviors

Every year, over 50 million people are injured and 1.35 million die in traffic accidents. Risky driving behaviors are responsible for over half of all fatal vehicle accidents. Identifying risky driving behaviors within real-world driving (RWD) datasets is a promising avenue to reduce the mortality burden associated with these unsafe behaviors, but numerous technical hurdles must be overcome to do so. Herein, we describe the implementation of a multistage process for classifying unlabeled RWD data as potentially risky or not. In the first stage, data are reformatted and reduced in preparation for classification. In the second stage, subsets of the reformatted data are labeled as potentially risky (or not) using the Iterative-DBSCAN method. In the third stage, the labeled subsets are then used to fit random forest (RF) classification models—RF models were chosen after they were found to be performing better than logistic regression and artificial neural network models. In the final stage, the RF models are used predictively to label the remaining RWD data as potentially risky (or not). The implementation of each stage is described and analyzed for the classification of RWD data from vehicles on public roads in Ann Arbor, Michigan. Overall, we identified 22.7 million observations of potentially risky driving out of 268.2 million observations. This study provides a novel approach for identifying potentially risky driving behaviors within RWD datasets. As such, this study represents an important step in the implementation of protocols designed to address and prevent the harms associated with risky driving.

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