loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Ali Al-Raziqi and Joachim Denzler

Affiliation: Friedrich Schiller University of Jena, Germany

ISBN: 978-989-758-175-5

Keyword(s): Interaction Detection, Multiple Object Tracking, Unsupervised Clustering, Hierarchical Dirichlet Processes.

Abstract: Extracting compound interactions involving multiple objects is a challenging task in computer vision due to different issues such as the mutual occlusions between objects, the varying group size and issues raised from the tracker. Additionally, the single activities are uncommon compared with the activities that are performed by two or more objects, e.g., gathering, fighting, running, etc. The purpose of this paper is to address the problem of interaction recognition among multiple objects based on dynamic features in an unsupervised manner. Our main contribution is twofold. First, a combined framework using a tracking-by-detection framework for trajectory extraction and HDPs for latent interaction extraction is introduced. Another important contribution is the introduction of a new dataset, the Cavy dataset. The Cavy dataset contains about six dominant interactions performed several times by two or three cavies at different locations. The cavies are interacting in complicate d and unexpected ways, which leads to perform many interactions in a short time. This makes working on this dataset more challenging. The experiments in this study are not only performed on the Cavy dataset but we also use the benchmark dataset Behave. The experiments on these datasets demonstrate the effectiveness of the proposed method. Although the our approach is completely unsupervised, we achieved satisfactory results with a clustering accuracy of up to 68.84% on the Behave dataset and up to 45% on the Cavy dataset. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 34.204.176.189

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Al-Raziqi, A. and Denzler, J. (2016). Unsupervised Framework for Interactions Modeling between Multiple Objects.In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 509-516. DOI: 10.5220/0005680705090516

@conference{visapp16,
author={Ali Al{-}Raziqi. and Joachim Denzler.},
title={Unsupervised Framework for Interactions Modeling between Multiple Objects},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={509-516},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005680705090516},
isbn={978-989-758-175-5},
}

TY - CONF

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Unsupervised Framework for Interactions Modeling between Multiple Objects
SN - 978-989-758-175-5
AU - Al-Raziqi, A.
AU - Denzler, J.
PY - 2016
SP - 509
EP - 516
DO - 10.5220/0005680705090516

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.