Embedded Real-Time Surveillance Using Multimodal Mean Background Modeling

2008 ◽  
pp. 163-175 ◽  
Author(s):  
Senyo Apewokin ◽  
Brian Valentine ◽  
Dana Forsthoefel ◽  
Linda Wills ◽  
Scott Wills ◽  
...  
Keyword(s):  
Real Time ◽  
Background Modeling

Real-Time Robust Background Modeling Based on Joint Color and Texture Descriptions

2010 ◽  
Author(s):  
Chih-Yang Lin ◽  
Chao-Chin Chang ◽  
Wei-Wen Chang ◽  
Meng-Hui Chen ◽  
Li-Wei Kang
Keyword(s):  
Real Time ◽  
Background Modeling

Real-Time Binary Descriptor Based Background Modeling

2013 ◽  
Author(s):  
Wan-Chen Liu ◽  
Shu-Zhe Lin ◽  
Min-Hsiang Yang ◽  
Chun-Rong Huang
Keyword(s):  
Real Time ◽  
Background Modeling ◽  
Binary Descriptor

GPU-based implementation of an optimized nonparametric background modeling for real-time moving object detection

2013 ◽  
Vol 59 (2) ◽  
pp. 361-369 ◽  
Author(s):  
Daniel Berjon ◽  
Carlos Cuevas ◽  
Francisco Moran ◽  
Narciso Garcia
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Background Modeling ◽  
Moving Object Detection ◽  
Moving Object

Real-Time Moving Object Detection based on Regional Background Modeling under a Moving Camera

2017 ◽  
Vol 61 (4) ◽  
pp. 405061-4050610
Author(s):  
Junhua Yan ◽  
Shunfei Wang ◽  
Wei Huang ◽  
Yongqi Xiao ◽  
Yong Yang
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Background Modeling ◽  
Moving Object Detection ◽  
Moving Object ◽  
Moving Camera ◽  
Regional Background

scholarly journals Real-Time Pedestrian Tracking and Counting with TLD

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Jiawei Shi ◽  
Xianmei Wang ◽  
Huer Xiao
Keyword(s):  
Real Time ◽  
Similarity Measure ◽  
Buffer Zone ◽  
Background Modeling ◽  
Single Line ◽  
Pedestrian Tracking ◽  
Public Database ◽  
The Public ◽  
Modeling Algorithm

This paper describes a solution to solve the issue of automatic multipedestrian tracking and counting. First, background modeling algorithm is applied to actively obtain multipedestrian candidates, followed by a confirmation step with classification. Then each pedestrian patch is handled by real-time TLD (Tracking-Learning-Detection) to get a new predication position according to similarity measure. Further TLD results are compared with classification list to determine a new, disappeared, or existing pedestrian. Finally single line counting with buffer zone is employed to count pedestrians. Experiments results on the public database, PETS, demonstrate the validity of our solution.

Real-time background modeling based on classified dynamic objects for human robot application

2012 ◽  
Author(s):  
Alessandro Moro ◽  
Enzo Mumolo ◽  
Massimiliano Nolich ◽  
Kenji Terabayashi ◽  
Kazunori Umeda
Keyword(s):  
Real Time ◽  
Background Modeling ◽  
Dynamic Objects
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