Tavanai, A, Sridhar, M, Gu, F et al. (2 more authors) (2013) Carried object detection and tracking using geometric shape models and spatio-temporal consistency. In: Chen, M, Leibe, B and Neumann, B, (eds.) Computer vision systems. Lecture notes in computer science. 9th International Conference, ICVS 2013, 16-18 Jul 2013, St Petersburg, Russia. Springer-Verlag , 223 - 233. ISBN 978-3-642-39401-0
Abstract
This paper proposes a novel approach that detects and tracks carried objects by modelling the person-carried object relationship that is characteristic of the carry event. In order to detect a generic class of carried objects, we propose the use of geometric shape models, instead of using pre-trained object class models or solely relying on protrusions. In order to track the carried objects, we propose a novel optimization procedure that combines spatio-temporal consistency characteristic of the carry event, with conventional properties such as appearance and motion smoothness respectively. The proposed approach substantially outperforms a state-of-the-art approach on two challenging datasets PETS2006 and MINDSEYE2012.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | (c) 2013, Springer-Verlag. This is an author produced version of a paper published in Computer Vision Systems: 9th International Conference, Icvs 2013, St. Petersburg, Russia, July 16-18, 2013. Proceedings (Lecture Notes in Computer Science). Uploaded in accordance with the publisher's self-archiving policy. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-39402-7_23 |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Nov 2014 10:23 |
Last Modified: | 19 Dec 2022 13:28 |
Published Version: | http://dx.doi.org/10.1007/978-3-642-39402-7_23 |
Status: | Published |
Publisher: | Springer-Verlag |
Identification Number: | 10.1007/978-3-642-39402-7_23 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:81159 |