The University of Southampton
University of Southampton Institutional Repository

People Detection using Gait for Visual Surveillance

People Detection using Gait for Visual Surveillance
People Detection using Gait for Visual Surveillance
Detecting, tracking and recognizing people using a single camera is a challenging problem due to occlusion, shadows, entry and exit of objects into the scene, and natural background clutter. Furthermore, the flexible structure of the human body, which encompasses a wide range of possible motion transformations, exacerbates difficulties for developing a vision-based surveillance system. We propose a multi-object tracking method based on feature correspondence between consecutive frames. Moving objects are assigned to different layers whereby blobs corresponding to the same object are assigned to the same layer. The criteria for allocating objects to layers is based on the Mahalanobis distance measure of shape-based features. Because of the dearth of visual surveillance systems that exploit human gait for object classification and their limited aim to detect people only using simple shape-based features extracted from silhouettes, we have explored an alternative technique for walking people detection based on their gait motion. The novelty of our approach is motivated by the latest research for people identification using gait
BOUCHRIKA, I.
794cc09f-700f-4902-b7e3-368e1819775e
NIXON, M. S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
BOUCHRIKA, I.
794cc09f-700f-4902-b7e3-368e1819775e
NIXON, M. S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

BOUCHRIKA, I. and NIXON, M. S. (2006) People Detection using Gait for Visual Surveillance. BMVA Symposium on Detection vs Tracking, London, United Kingdom.

Record type: Conference or Workshop Item (Paper)

Abstract

Detecting, tracking and recognizing people using a single camera is a challenging problem due to occlusion, shadows, entry and exit of objects into the scene, and natural background clutter. Furthermore, the flexible structure of the human body, which encompasses a wide range of possible motion transformations, exacerbates difficulties for developing a vision-based surveillance system. We propose a multi-object tracking method based on feature correspondence between consecutive frames. Moving objects are assigned to different layers whereby blobs corresponding to the same object are assigned to the same layer. The criteria for allocating objects to layers is based on the Mahalanobis distance measure of shape-based features. Because of the dearth of visual surveillance systems that exploit human gait for object classification and their limited aim to detect people only using simple shape-based features extracted from silhouettes, we have explored an alternative technique for walking people detection based on their gait motion. The novelty of our approach is motivated by the latest research for people identification using gait

Text
paper-bmva-symp-2006.pdf - Other
Download (474kB)

More information

Published date: 2006
Additional Information: Event Dates: July, 2006
Venue - Dates: BMVA Symposium on Detection vs Tracking, London, United Kingdom, 2006-06-30
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 262817
URI: http://eprints.soton.ac.uk/id/eprint/262817
PURE UUID: eb8e61a5-e5dd-4573-968f-6edc36986ade
ORCID for M. S. NIXON: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 07 Jul 2006
Last modified: 07 Oct 2020 02:38

Export record

Contributors

Author: I. BOUCHRIKA
Author: M. S. NIXON ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×