Real-Time Multi-Level Digital Compositing—Quality Issues

1988 ◽  
Author(s):  
Peter D. Symes
Keyword(s):  
Real Time ◽  
Multi Level ◽  
Quality Issues ◽  
Digital Compositing

Real-Time Multilevel Digital Compositing: Quality Issues

SMPTE Journal ◽  
1989 ◽  
Vol 98 (5) ◽  
pp. 376-378
Author(s):  
Peter D. Symes
Keyword(s):  
Real Time ◽  
Quality Issues ◽  
Digital Compositing

scholarly journals Deeper Siamese network with multi‐level feature fusion for real‐time visual tracking

2019 ◽  
Vol 55 (13) ◽  
pp. 742-745 ◽  
Author(s):  
Kang Yang ◽  
Huihui Song ◽  
Kaihua Zhang ◽  
Jiaqing Fan
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Feature Fusion ◽  
Siamese Network ◽  
Multi Level

Driver inattention monitoring system based on multimodal fusion with visual cues to improve driving safety

2016 ◽  
Vol 40 (3) ◽  
pp. 885-895 ◽  
Author(s):  
Xuanpeng Li ◽  
Emmanuel Seignez
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Visual Cues ◽  
Real Life ◽  
Multimodal Fusion ◽  
Driving Safety ◽  
Driver Drowsiness ◽  
Driver Monitoring ◽  
Driver Inattention ◽  
Multi Level

Driver inattention, either driver drowsiness or distraction, is a major contributor to serious traffic crashes. In general, most research on this topic studies driver drowsiness and distraction separately, and is often conducted in a well-controlled, simulated environment. By considering the reliability and flexibility of real-time driver monitoring systems, it is possible to evaluate driver inattention by the fusion of multiple selected cues in real life scenarios. This paper presents a real-time, visual-cue-based driver monitoring system, which can track both multi-level driver drowsiness and distraction simultaneously. A set of visual cues are adopted via analysis of drivers’ physical behaviour and driving performance. Driver drowsiness is evaluated using a multi-level scale, by applying evidence theory. Additionally, a general framework of extensive hierarchical combinations is used to generate a probabilistic evaluation of driving risk in real time. This driver inattention monitoring system with multimodal fusion has been proven to improve the accuracy of risk evaluation and reduce the rate of false alarms, and acceptance of the system is recommended.

scholarly journals Real-Time Action Recognition Using Multi-level Action Descriptor and DNN

2019 ◽  
Author(s):  
Cheng-Bin Jin ◽  
Trung Dung Do ◽  
Mingjie Liu ◽  
Hakil Kim
Keyword(s):  
Real Time ◽  
Action Recognition ◽  
Multi Level
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