loading
Documents

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Muhammad Owais Mehmood 1 ; Sébastien Ambellouis 1 and Catherine Achard 2

Affiliations: 1 French Institute of Science and Technology for Transport, Spatial Planning and Development and Networks, France ; 2 Sorbonne Universites, UPMC Univ Paris 06 and CNRS UMR 7222 and ISIR, France

ISBN: 978-989-758-090-1

Keyword(s): People Localization, Ghost Pruning, Multi-camera Surveillance, Shape Representations, Pattern Recognition.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Camera Networks and Vision ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Motion, Tracking and Stereo Vision ; Shape Representation and Matching ; Video Surveillance and Event Detection

Abstract: We present a method for multi-camera people detection based on the multi-view geometry. We propose to create a synergy map by the projection of foreground masks across all camera views on the ground plane and the planes parallel to the ground. This leads to significant values on locations where people are present, and also to a particular shape around these values. Moreover, a well-known ghost phenomena appears i.e. when these shapes corresponding to different persons are fused then the false detections are also generated. In this article, the first improvement is the robust detection of the candidate detection locations, namely keypoints, from the synergy map based on a watershed transform. Then, in order to reduce the false positives, mainly due to the ghost phenomena, we check if the particular shape, for an ideal person, is present or not. This shape, that is different for each location of the synergy map, is generated for each keypoint, assuming the presence of a person, and with the knowledge of the scene geometry. Finally, the real shape and the synthetic one are compared using a similarity measure that is similar to correlation. Another improvement proposed in this article is the use of unsupervised clustering, performed on the measures obtained at all the keypoints. It allows to automatically find the optimal threshold on the measure, and thus to decide about people detection. We have compared our method to the recent state-of-the-art techniques on a publicly available dataset and have shown that it reduces the detection errors. (More)

PDF ImageFull Text

Download
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 34.201.121.213

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:
Owais Mehmood, M.; Ambellouis, S.; Achard, C. and Achard, C. (2015). Launch These Manhunts! Shaping the Synergy Maps for Multi-camera Detection.In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 528-535. DOI: 10.5220/0005355805280535

@conference{visapp15,
author={Muhammad Owais Mehmood. and Sébastien Ambellouis. and Catherine Achard. and Catherine Achard.},
title={Launch These Manhunts! Shaping the Synergy Maps for Multi-camera Detection},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={528-535},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005355805280535},
isbn={978-989-758-090-1},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Launch These Manhunts! Shaping the Synergy Maps for Multi-camera Detection
SN - 978-989-758-090-1
AU - Owais Mehmood, M.
AU - Ambellouis, S.
AU - Achard, C.
AU - Achard, C.
PY - 2015
SP - 528
EP - 535
DO - 10.5220/0005355805280535

Login or register to post comments.

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