Background initialisation by spatio-temporal motion estimation

Sriram Varadarajan; H. Wang; Bryan Scotney; O Nibouche

doi:10.1109/AVSS.2017.8078485

Background initialisation by spatio-temporal motion estimation

Sriram Varadarajan
, H. Wang
, Bryan Scotney
, O Nibouche

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Estimating the initial background of a scene is a key prerequisite for several applications in video analytics. In this paper, we present a simple approach that takes into account spatio-temporal motion intensities while estimating the true background. We tested the algorithm on real video sequences from the Scene Background Initialization (SBI) benchmark dataset, and the results show that the algorithm is competitive compared to the state of the art.

Original language	English
Title of host publication	14th IEEE International Conference on Advanced Video and Signal Based Surveillance
ISBN (Electronic)	978-1-5386-2939-0
DOIs	https://doi.org/10.1109/AVSS.2017.8078485
Publication status	Published (in print/issue) - 23 Oct 2017
Event	14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) - Lecce, Italy Duration: 29 Aug 2017 → 1 Sept 2017 https://ieeexplore.ieee.org/xpl/conhome/8055736/proceeding

Conference

Conference	14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
City	Italy
Period	29/08/17 → 1/09/17
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8055736/proceeding

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 11 Sustainable Cities and Communities

Keywords

Video sequences
Measurement
Estimation
Image color analysis
Clustering algorithms
Neural networks
Adaptation models

Access to Document

10.1109/AVSS.2017.8078485

Cite this

@inproceedings{a1cd808e1bed4b8db55c15a0c70c411a,

title = "Background initialisation by spatio-temporal motion estimation",

abstract = "Estimating the initial background of a scene is a key prerequisite for several applications in video analytics. In this paper, we present a simple approach that takes into account spatio-temporal motion intensities while estimating the true background. We tested the algorithm on real video sequences from the Scene Background Initialization (SBI) benchmark dataset, and the results show that the algorithm is competitive compared to the state of the art.",

keywords = "Video sequences, Measurement, Estimation, Image color analysis, Clustering algorithms, Neural networks, Adaptation models",

author = "Sriram Varadarajan and H. Wang and Bryan Scotney and O Nibouche",

year = "2017",

month = oct,

day = "23",

doi = "10.1109/AVSS.2017.8078485",

language = "English",

isbn = "978-1-5386-2940-6",

booktitle = "14th IEEE International Conference on Advanced Video and Signal Based Surveillance",

note = "14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) ; Conference date: 29-08-2017 Through 01-09-2017",

url = "https://ieeexplore.ieee.org/xpl/conhome/8055736/proceeding",

}

TY - GEN

T1 - Background initialisation by spatio-temporal motion estimation

AU - Varadarajan, Sriram

AU - Wang, H.

AU - Scotney, Bryan

AU - Nibouche, O

PY - 2017/10/23

Y1 - 2017/10/23

N2 - Estimating the initial background of a scene is a key prerequisite for several applications in video analytics. In this paper, we present a simple approach that takes into account spatio-temporal motion intensities while estimating the true background. We tested the algorithm on real video sequences from the Scene Background Initialization (SBI) benchmark dataset, and the results show that the algorithm is competitive compared to the state of the art.

AB - Estimating the initial background of a scene is a key prerequisite for several applications in video analytics. In this paper, we present a simple approach that takes into account spatio-temporal motion intensities while estimating the true background. We tested the algorithm on real video sequences from the Scene Background Initialization (SBI) benchmark dataset, and the results show that the algorithm is competitive compared to the state of the art.

KW - Video sequences

KW - Measurement

KW - Estimation

KW - Image color analysis

KW - Clustering algorithms

KW - Neural networks

KW - Adaptation models

U2 - 10.1109/AVSS.2017.8078485

DO - 10.1109/AVSS.2017.8078485

M3 - Conference contribution

SN - 978-1-5386-2940-6

BT - 14th IEEE International Conference on Advanced Video and Signal Based Surveillance

T2 - 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

Y2 - 29 August 2017 through 1 September 2017

ER -

Background initialisation by spatio-temporal motion estimation

Abstract

Conference

UN SDGs

Keywords

Access to Document

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Cite this