A real-time algorithm of dynamic background extraction in image sequence

2005 ◽  
Author(s):  
Yong Fan
Keyword(s):  
Real Time ◽  
Image Sequence ◽  
Time Algorithm ◽  
Dynamic Background ◽  
Background Extraction

Real-Time Objects Detection Using Layered Codebook Model

2013 ◽  
Vol 694-697 ◽  
pp. 1937-1944
Author(s):  
Dan Yan ◽  
Qiang Yu ◽  
Ming Hui Wang
Keyword(s):  
Real Time ◽  
Surveillance System ◽  
Time Algorithm ◽  
High Accuracy ◽  
Background Model ◽  
Dynamic Background ◽  
Frame Difference ◽  
Illumination Changes ◽  
Background Segmentation ◽  
Objects Detection

In surveillance system,it was challenging to improve real-time in the presence of dynamic background motions.We presented a real-time algorithm for foreground-background segmentation based on codebook model.Pixels were converted from RGB space to YCrCb space,background model used layered model.Firstly we established a basic codebook background model and then got rough background pixels by twice frame difference,and then only trained rough background pixels which have removed foreground pixels.Secondly the foreground was segmented from the background and we updated the background in real-time. The experimental results show that this method can save time of establishment of codebook background model and has small calculation and high accuracy in scenes such as illumination changes,swaying trees and stopped objects should be considered part of the background objects.

A Distributed Real-Time Algorithm for Preference-Based Agreement

2011 ◽  
Vol 44 (1) ◽  
pp. 8933-8938
Author(s):  
Daniel Zelazo ◽  
Mathias Bürger ◽  
Frank Allgöwer
Keyword(s):  
Real Time ◽  
Time Algorithm

scholarly journals Real-Time Algorithm for Versatile Displacement Sensors Based on Self-Mixing Interferometry

2016 ◽  
Vol 16 (1) ◽  
pp. 195-202 ◽  
Author(s):  
Antonio Luna Arriaga ◽  
Francis Bony ◽  
Thierry Bosch
Keyword(s):  
Real Time ◽  
Time Algorithm ◽  
Displacement Sensors

Detecting Pedestrian Situation Awareness in Real-Time: Algorithm Development Using Heart Rate Variability and Phone Position

2018 ◽  
Vol 62 (1) ◽  
pp. 1579-1583
Author(s):  
Yourui Tong ◽  
Bochen Jia ◽  
Yi Wang ◽  
Si Yang
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Real Time ◽  
Situation Awareness ◽  
Mental State ◽  
Time Algorithm ◽  
Support Vector ◽  
Vehicle Design ◽  
Automated Vehicle ◽  
Hrv Analysis

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.

A real-time algorithm for fault identification in machining centres

2011 ◽  
Vol 44 (1) ◽  
pp. 5201-5206 ◽  
Author(s):  
L. Ferrarini ◽  
M. Allevi ◽  
A. Dedè
Keyword(s):  
Real Time ◽  
Time Algorithm ◽  
Fault Identification

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

2017 ◽  
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.

scholarly journals A Flexible, Real-Time Algorithm for Simulating Correlated Random Fields and Its Properties

2017 ◽  
Vol 13 (3) ◽  
pp. 197-208 ◽  
Author(s):  
Michael A. Kouritzin ◽  
Fraser Newton ◽  
Biao Wu
Keyword(s):  
Real Time ◽  
Random Fields ◽  
Time Algorithm
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