Detection and tracking of traffic signs using a recursive Bayesian decision framework

2011 ◽  
Author(s):  
Javier Marinas ◽  
Luis Salgado ◽  
Jon Arrospide ◽  
Marcos Nieto
Keyword(s):  
Bayesian Decision ◽  
Decision Framework ◽  
Traffic Signs ◽  
Detection And Tracking ◽  
Recursive Bayesian

scholarly journals Where Is the Clean Air? A Bayesian Decision Framework for Personalised Cyclist Route Selection Using R-INLA

2021 ◽  
Vol 16 (1) ◽  
pp. 61-91 ◽  
Author(s):  
Laura C. Dawkins ◽  
Daniel B. Williamson ◽  
Kerrie L. Mengersen ◽  
Lidia Morawska ◽  
Rohan Jayaratne ◽  
...  
Keyword(s):  
Route Selection ◽  
Bayesian Decision ◽  
Decision Framework ◽  
Clean Air

scholarly journals A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs

2021 ◽  
Vol 11 (7) ◽  
pp. 3061
Author(s):  
Shaojian Song ◽  
Yuanchao Li ◽  
Qingbao Huang ◽  
Gang Li
Keyword(s):  
Real Time ◽  
Data Augmentation ◽  
Input Image ◽  
Small Sample ◽  
Traffic Signs ◽  
Feature Map ◽  
Detection And Tracking ◽  
Detailed Method ◽  
Tracking Ability ◽  
Small Targets

It is a challenging task for self-driving vehicles in Real-World traffic scenarios to find a trade-off between the real-time performance and the high accuracy of the detection, recognition, and tracking in videos. This issue is addressed in this paper with an improved YOLOv3 (You Only Look Once) and a multi-object tracking algorithm (Deep-Sort). First, data augmentation is employed for small sample traffic signs to address the problem of an extremely unbalanced distribution of different samples in the dataset. Second, a new architecture of YOLOv3 is proposed to make it more suitable for detecting small targets. The detailed method is (1) removing the output feature map corresponding to the 32-times subsampling of the input image in the original YOLOv3 structure to reduce its computational costs and improve its real-time performances; (2) adding an output feature map of 4-times subsampling to improve its detection capability for the small traffic signs; (3) Deep-Sort is integrated into the detection method to improve the precision and robustness of multi-object detection, and the tracking ability in videos. Finally, our method demonstrated better detection capabilities, with respect to state-of-the-art approaches, which precision, recall and mAP is 91%, 90%, and 84.76% respectively.

scholarly journals Optimal crowd-augmented spectrum mapping via an iterative Bayesian decision framework

2020 ◽  
Vol 105 ◽  
pp. 102163
Author(s):  
Ahmad Rabanimotlagh ◽  
Prabhu Janakaraj ◽  
Pu Wang
Keyword(s):  
Bayesian Decision ◽  
Decision Framework

LPCD framework: Analytical tool or psychological model?

2018 ◽  
Vol 41 ◽  
Author(s):  
David Danks
Keyword(s):  
Decision Making ◽  
Analytical Tool ◽  
Mathematical Framework ◽  
Bayesian Decision ◽  
Perceptual Decision Making ◽  
Psychological Model ◽  
Psychological Reality ◽  
Psychological Theories ◽  
Target Article ◽  
Bayesian Decision Making

AbstractThe target article uses a mathematical framework derived from Bayesian decision making to demonstrate suboptimal decision making but then attributes psychological reality to the framework components. Rahnev & Denison's (R&D) positive proposal thus risks ignoring plausible psychological theories that could implement complex perceptual decision making. We must be careful not to slide from success with an analytical tool to the reality of the tool components.

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