Tavanai, A, Sridhar, M, Gu, F et al. (2 more authors) (2014) Context aware detection and tracking. In: Proceedings - International Conference on Pattern Recognition. 2014 22nd International Conference on Pattern Recognition (ICPR), 24-28 Aug 2014, Stockholm. IEEE , 2197 - 2202. ISBN 9781479952083
Abstract
This paper presents a novel approach to incorporate multiple contextual factors into a tracking process, for the purpose of reducing false positive detections. While much previous work has focused on improving object detection on static images using context, these have not been integrated into the tracking process. Our hypothesis is that a significant improvement can result from the use of context in dynamically influencing the linking of object detections, during the tracking process. To verify this hypothesis, we augment a state of the art dynamic programming based tracker with contextual information by reformulating the maximum a posteriori (MAP) estimation formulation. This formulation introduces contextual factors that first of all augment detection strengths and secondly provides temporal context. We allow both these types of factors to contribute organically to the linking process by learning the relative contribution of each of these factors jointly during a gradient decent based optimisation process. Our experiments demonstrate that the proposed approach contributes to a significantly superior performance on a recent challenging video dataset, which captures complex scenes with a wide range of object types and diverse backgrounds.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works |
Dates: |
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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: | 31 Mar 2015 14:47 |
Last Modified: | 19 Dec 2022 13:30 |
Published Version: | http://dx.doi.org/10.1109/ICPR.2014.382 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICPR.2014.382 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:83875 |