Recovering background regions in videos of cluttered urban scenes

Iain Rodger; Barry Connor; Neil M. Robertson

doi:10.1109/ICIP.2015.7351576

Recovering background regions in videos of cluttered urban scenes

Iain Rodger, Barry Connor, Neil M. Robertson

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

1 Citation (Scopus)

Abstract

In this paper we present a novel segmentation technique and adapt it for enhanced background recovery in crowded urban scenes. Building on an initial superpixels representation, smaller regions are merged depending on their perceived similarity to develop larger regions. Based on the observation that human activity tends to be quite structured, during this process we exploit emerging foreground context (tracks of people) to influence the segmentation process via Bayesian priors. These priors incorporate both temporal and spatial smoothing. We validate the approach on benchmarked urban datasets and show that our method improves on established image segmentation methods.

Original language	English
Title of host publication	2015 IEEE International Conference on Image Processing (ICIP)
Subtitle of host publication	Proceedings
Publisher	IEEE
Pages	4097-4101
Number of pages	5
ISBN (Print)	9781479983391
DOIs	https://doi.org/10.1109/ICIP.2015.7351576
Publication status	Published - 9 Dec 2015
Event	22nd IEEE International Conference on Image Processing 2015 - Quebec City, Canada Duration: 27 Sept 2015 → 30 Sept 2015 Conference number: 22

Conference

Conference	22nd IEEE International Conference on Image Processing 2015
Abbreviated title	ICIP 2015
Country/Territory	Canada
City	Quebec City
Period	27/09/15 → 30/09/15

Keywords

Foreground Context
Image Segmentation
Pattern Recognition
Superpixels

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

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10.1109/ICIP.2015.7351576

Cite this

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title = "Recovering background regions in videos of cluttered urban scenes",

abstract = "In this paper we present a novel segmentation technique and adapt it for enhanced background recovery in crowded urban scenes. Building on an initial superpixels representation, smaller regions are merged depending on their perceived similarity to develop larger regions. Based on the observation that human activity tends to be quite structured, during this process we exploit emerging foreground context (tracks of people) to influence the segmentation process via Bayesian priors. These priors incorporate both temporal and spatial smoothing. We validate the approach on benchmarked urban datasets and show that our method improves on established image segmentation methods.",

keywords = "Foreground Context, Image Segmentation, Pattern Recognition, Superpixels",

author = "Iain Rodger and Barry Connor and Robertson, {Neil M.}",

year = "2015",

month = dec,

day = "9",

doi = "10.1109/ICIP.2015.7351576",

language = "English",

isbn = "9781479983391",

pages = "4097--4101",

booktitle = "2015 IEEE International Conference on Image Processing (ICIP)",

publisher = "IEEE",

address = "United States",

note = "22nd IEEE International Conference on Image Processing 2015, ICIP 2015 ; Conference date: 27-09-2015 Through 30-09-2015",

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TY - GEN

T1 - Recovering background regions in videos of cluttered urban scenes

AU - Rodger, Iain

AU - Connor, Barry

AU - Robertson, Neil M.

N1 - Conference code: 22

PY - 2015/12/9

Y1 - 2015/12/9

N2 - In this paper we present a novel segmentation technique and adapt it for enhanced background recovery in crowded urban scenes. Building on an initial superpixels representation, smaller regions are merged depending on their perceived similarity to develop larger regions. Based on the observation that human activity tends to be quite structured, during this process we exploit emerging foreground context (tracks of people) to influence the segmentation process via Bayesian priors. These priors incorporate both temporal and spatial smoothing. We validate the approach on benchmarked urban datasets and show that our method improves on established image segmentation methods.

AB - In this paper we present a novel segmentation technique and adapt it for enhanced background recovery in crowded urban scenes. Building on an initial superpixels representation, smaller regions are merged depending on their perceived similarity to develop larger regions. Based on the observation that human activity tends to be quite structured, during this process we exploit emerging foreground context (tracks of people) to influence the segmentation process via Bayesian priors. These priors incorporate both temporal and spatial smoothing. We validate the approach on benchmarked urban datasets and show that our method improves on established image segmentation methods.

KW - Foreground Context

KW - Image Segmentation

KW - Pattern Recognition

KW - Superpixels

UR - http://www.scopus.com/inward/record.url?scp=84956698702&partnerID=8YFLogxK

U2 - 10.1109/ICIP.2015.7351576

DO - 10.1109/ICIP.2015.7351576

M3 - Conference contribution

AN - SCOPUS:84956698702

SN - 9781479983391

SP - 4097

EP - 4101

BT - 2015 IEEE International Conference on Image Processing (ICIP)

PB - IEEE

T2 - 22nd IEEE International Conference on Image Processing 2015

Y2 - 27 September 2015 through 30 September 2015

ER -

Recovering background regions in videos of cluttered urban scenes

Abstract

Conference

Keywords

ASJC Scopus subject areas

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