A GPU-Based Real-Time Algorithm for Virtual Viewpoint Rendering from Multi-video

To help automated vehicles learn surrounding environments via V2X communications, it is important to detect and transfer pedestrian situation awareness to the related vehicles. Based on the characteristics of pedestrians, a real-time algorithm was developed to detect pedestrian situation awareness. In the study, the heart rate variability (HRV) and phone position were used to understand the mental state and distractions of pedestrians. The HRV analysis was used to detect the fatigue and alert state of the pedestrian, and the phone position was used to define the phone distractions of the pedestrian. A Support Vector Machine algorithm was used to classify the pedestrian’s mental state. The results indicated a good performance with 86% prediction accuracy. The developed algorithm shows high applicability to detect the pedestrian’s situation awareness in real-time, which would further extend our understanding on V2X employment and automated vehicle design.

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A real-time algorithm for fault identification in machining centres

IFAC Proceedings Volumes ◽

10.3182/20110828-6-it-1002.02021 ◽

2011 ◽

Vol 44 (1) ◽

pp. 5201-5206 ◽

Cited By ~ 1

Author(s):

L. Ferrarini ◽

M. Allevi ◽

A. Dedè

Keyword(s):

Real Time ◽

Time Algorithm ◽

Fault Identification

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Proving a real time algorithm for ATM in Coq

Lecture Notes in Computer Science - Types for Proofs and Programs ◽

10.1007/bfb0097797 ◽

1998 ◽

pp. 277-293 ◽

Cited By ~ 5

Author(s):

Jean-François Monin

Keyword(s):

Real Time ◽

Time Algorithm

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Aggressive, Tense or Shy? Identifying Personality Traits from Crowd Videos

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/17 ◽

2017 ◽

Cited By ~ 9

Author(s):

Aniket Bera ◽

Tanmay Randhavane ◽

Dinesh Manocha

Keyword(s):

Personality Traits ◽

Real Time ◽

Personality Trait ◽

User Study ◽

Movement Patterns ◽

Time Algorithm ◽

High Density ◽

Motion Model ◽

Classification Criterion ◽

Trait Theory

We present a real-time algorithm to automatically classify the behavior or personality of a pedestrian based on his or her movements in a crowd video. Our classification criterion is based on Personality Trait theory. We present a statistical scheme that dynamically learns the behavior of every pedestrian and computes its motion model. This model is combined with global crowd characteristics to compute the movement patterns and motion dynamics and use them for crowd prediction. Our learning scheme is general and we highlight its performance in identifying the personality of different pedestrians in low and high density crowd videos. We also evaluate the accuracy by comparing the results with a user study.

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