Counting crowded moving objects

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Standard

Counting crowded moving objects. / Rabaud, Vincent; Belongie, Serge.

I: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, s. 705-711.

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Harvard

Rabaud, V & Belongie, S 2006, 'Counting crowded moving objects', Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, s. 705-711. https://doi.org/10.1109/CVPR.2006.92

APA

Rabaud, V., & Belongie, S. (2006). Counting crowded moving objects. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 705-711. https://doi.org/10.1109/CVPR.2006.92

Vancouver

Rabaud V, Belongie S. Counting crowded moving objects. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006;705-711. https://doi.org/10.1109/CVPR.2006.92

Author

Rabaud, Vincent ; Belongie, Serge. / Counting crowded moving objects. I: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006 ; s. 705-711.

Bibtex

@inproceedings{b94d07e2a8584d6b9861d024a13212d6,
title = "Counting crowded moving objects",
abstract = "In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably with occlusions and therefore with crowding. When the objects are constrained to be of the same kind, however, partitioning of densely crowded semi-rigid objects can be accomplished by means of clustering tracked feature points. We base our approach on a highly parallelized version of the KLT tracker in order to process the video into a set of feature trajectories. While such a set of trajectories provides a substrate for motion analysis, their unequal lengths and fragmented nature present difficulties for subsequent processing. To address this, we propose a simple means of spatially and temporally conditioning the trajectories. Given this representation, we integrate it with a learned object descriptor to achieve a segmentation of the constituent motions. We present experimental results for the problem of estimating the number of moving objects in a dense crowd as a function of time.",
author = "Vincent Rabaud and Serge Belongie",
year = "2006",
doi = "10.1109/CVPR.2006.92",
language = "English",
pages = "705--711",
journal = "I E E E Conference on Computer Vision and Pattern Recognition. Proceedings",
issn = "1063-6919",
publisher = "Institute of Electrical and Electronics Engineers",
note = "2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006 ; Conference date: 17-06-2006 Through 22-06-2006",

}

RIS

TY - GEN

T1 - Counting crowded moving objects

AU - Rabaud, Vincent

AU - Belongie, Serge

PY - 2006

Y1 - 2006

N2 - In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably with occlusions and therefore with crowding. When the objects are constrained to be of the same kind, however, partitioning of densely crowded semi-rigid objects can be accomplished by means of clustering tracked feature points. We base our approach on a highly parallelized version of the KLT tracker in order to process the video into a set of feature trajectories. While such a set of trajectories provides a substrate for motion analysis, their unequal lengths and fragmented nature present difficulties for subsequent processing. To address this, we propose a simple means of spatially and temporally conditioning the trajectories. Given this representation, we integrate it with a learned object descriptor to achieve a segmentation of the constituent motions. We present experimental results for the problem of estimating the number of moving objects in a dense crowd as a function of time.

AB - In its full generality, motion analysis of crowded objects necessitates recognition and segmentation of each moving entity. The difficulty of these tasks increases considerably with occlusions and therefore with crowding. When the objects are constrained to be of the same kind, however, partitioning of densely crowded semi-rigid objects can be accomplished by means of clustering tracked feature points. We base our approach on a highly parallelized version of the KLT tracker in order to process the video into a set of feature trajectories. While such a set of trajectories provides a substrate for motion analysis, their unequal lengths and fragmented nature present difficulties for subsequent processing. To address this, we propose a simple means of spatially and temporally conditioning the trajectories. Given this representation, we integrate it with a learned object descriptor to achieve a segmentation of the constituent motions. We present experimental results for the problem of estimating the number of moving objects in a dense crowd as a function of time.

UR - http://www.scopus.com/inward/record.url?scp=33845571601&partnerID=8YFLogxK

U2 - 10.1109/CVPR.2006.92

DO - 10.1109/CVPR.2006.92

M3 - Conference article

AN - SCOPUS:33845571601

SP - 705

EP - 711

JO - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings

JF - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings

SN - 1063-6919

T2 - 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006

Y2 - 17 June 2006 through 22 June 2006

ER -

ID: 302053753