Detection of vehicle approach in the presence of additional motion and simulated observer motion at road junctions.

2013 ◽  
Vol 19 (2) ◽  
pp. 171-184 ◽  
Author(s):  
Mark Gould ◽  
Damian R. Poulter ◽  
Shaun Helman ◽  
John P. Wann
Keyword(s):  
Observer Motion ◽  
Road Junctions

A Heuristic and Decentralized Approach to Intelligent Traffic Management for Low-Density Road Junctions

2021 ◽  
Author(s):  
Jayde Paolo C. Mirandilla ◽  
Elmer R. Magsino ◽  
Gerald P. Arada ◽  
Reggie C. Gustilo
Keyword(s):  
Traffic Management ◽  
Low Density ◽  
Road Junctions

scholarly journals Improving Human Motion Classification by Applying Bagging and Symmetry to PCA-Based Features

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1264 ◽  
Author(s):  
Tomasz Hachaj
Keyword(s):  
Nearest Neighbor ◽  
Recognition Rate ◽  
Principal Component ◽  
Human Motion ◽  
Support Vector ◽  
Human Observer ◽  
Weak Classifier ◽  
Motion Classification ◽  
Observer Motion ◽  
Human Motion Classification

This paper proposes a method for improving human motion classification by applying bagging and symmetry to Principal Component Analysis (PCA)-based features. In contrast to well-known bagging algorithms such as random forest, the proposed method recalculates the motion features for each “weak classifier” (it does not randomly sample a feature set). The proposed classification method was evaluated on a challenging (even to a human observer) motion capture recording dataset of martial arts techniques performed by professional karate sportspeople. The dataset consisted of 360 recordings in 12 motion classes. Because some classes of these motions might be symmetrical (which means that they are performed with a dominant left or right hand/leg), an analysis was conducted to determine whether accounting for symmetry could improve the recognition rate of a classifier. The experimental results show that applying the proposed classifiers’ bagging procedure increased the recognition rate (RR) of the Nearest-Neighbor (NNg) and Support Vector Machine (SVM) classifiers by more than 5% and 3%, respectively. The RR of one trained classifier (SVM) was higher when we did not use symmetry. On the other hand, the application of symmetry information for bagged NNg improved its recognition rate compared with the results without symmetry information. We can conclude that symmetry information might be helpful in situations in which it is not possible to optimize the decision borders of the classifier (for example, when we do not have direct information about class labels). The experiment presented in this paper shows that, in this case, bagging and mirroring might help find a similar object in the training set that shares the same class label. Both the dataset that was used for the evaluation and the implementation of the proposed method can be downloaded, so the experiment is easily reproducible.

Regulation of Traffic Lights at Road Junctions

2009 ◽  
Author(s):  
Alfredo Cutolo ◽  
Rosanna Manzo ◽  
Luigi Rarità ◽  
George Maroulis ◽  
Theodore E. Simos
Keyword(s):  
Traffic Lights ◽  
Road Junctions

scholarly journals Psychophysical evidence for auditory motion parallax

2018 ◽  
Vol 115 (16) ◽  
pp. 4264-4269 ◽  
Author(s):  
Daria Genzel ◽  
Michael Schutte ◽  
W. Owen Brimijoin ◽  
Paul R. MacNeilage ◽  
Lutz Wiegrebe
Keyword(s):  
Motion Parallax ◽  
Relative Depth ◽  
Auditory Motion ◽  
Motion Platform ◽  
Sound Sources ◽  
Binaural Cues ◽  
Binaural Processing ◽  
Perceptual Strategy ◽  
Observer Motion ◽  
Self Motion

Distance is important: From an ecological perspective, knowledge about the distance to either prey or predator is vital. However, the distance of an unknown sound source is particularly difficult to assess, especially in anechoic environments. In vision, changes in perspective resulting from observer motion produce a reliable, consistent, and unambiguous impression of depth known as motion parallax. Here we demonstrate with formal psychophysics that humans can exploit auditory motion parallax, i.e., the change in the dynamic binaural cues elicited by self-motion, to assess the relative depths of two sound sources. Our data show that sensitivity to relative depth is best when subjects move actively; performance deteriorates when subjects are moved by a motion platform or when the sound sources themselves move. This is true even though the dynamic binaural cues elicited by these three types of motion are identical. Our data demonstrate a perceptual strategy to segregate intermittent sound sources in depth and highlight the tight interaction between self-motion and binaural processing that allows assessment of the spatial layout of complex acoustic scenes.

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