IEICE TRANSACTIONS on Information
Measuring Collectiveness in Crowded Scenes via Link Prediction
Summary :
Collective motion stems from the coordinated behaviors among individuals of crowds, and has attracted growing interest from the physics and computer vision communities. Collectiveness is a metric of the degree to which the state of crowd motion is ordered or synchronized. In this letter, we present a scheme to measure collectiveness via link prediction. Toward this aim, we propose a similarity index called superposed random walk with restarts (SRWR) and construct a novel collectiveness descriptor using the SRWR index and the Laplacian spectrum of a network. Experiments show that our approach gives promising results in real-world crowd scenes, and performs better than the state-of-the-art methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.8 pp.1617-1620
- Publication Date
- 2015/08/01
- Publicized
- 2015/05/14
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2015EDL8011
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Jun JIANG
Sichuan University,Southwest Petroleum University
Di WU
Sichuan University
Qizhi TENG
Sichuan University
Xiaohai HE
Sichuan University
Mingliang GAO
Shandong University of Technology
Keyword
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Jun JIANG, Di WU, Qizhi TENG, Xiaohai HE, Mingliang GAO, "Measuring Collectiveness in Crowded Scenes via Link Prediction" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 8, pp. 1617-1620, August 2015, doi: 10.1587/transinf.2015EDL8011.
Abstract: Collective motion stems from the coordinated behaviors among individuals of crowds, and has attracted growing interest from the physics and computer vision communities. Collectiveness is a metric of the degree to which the state of crowd motion is ordered or synchronized. In this letter, we present a scheme to measure collectiveness via link prediction. Toward this aim, we propose a similarity index called superposed random walk with restarts (SRWR) and construct a novel collectiveness descriptor using the SRWR index and the Laplacian spectrum of a network. Experiments show that our approach gives promising results in real-world crowd scenes, and performs better than the state-of-the-art methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDL8011/_p
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@ARTICLE{e98-d_8_1617,
author={Jun JIANG, Di WU, Qizhi TENG, Xiaohai HE, Mingliang GAO, },
journal={IEICE TRANSACTIONS on Information},
title={Measuring Collectiveness in Crowded Scenes via Link Prediction},
year={2015},
volume={E98-D},
number={8},
pages={1617-1620},
abstract={Collective motion stems from the coordinated behaviors among individuals of crowds, and has attracted growing interest from the physics and computer vision communities. Collectiveness is a metric of the degree to which the state of crowd motion is ordered or synchronized. In this letter, we present a scheme to measure collectiveness via link prediction. Toward this aim, we propose a similarity index called superposed random walk with restarts (SRWR) and construct a novel collectiveness descriptor using the SRWR index and the Laplacian spectrum of a network. Experiments show that our approach gives promising results in real-world crowd scenes, and performs better than the state-of-the-art methods.},
keywords={},
doi={10.1587/transinf.2015EDL8011},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Measuring Collectiveness in Crowded Scenes via Link Prediction
T2 - IEICE TRANSACTIONS on Information
SP - 1617
EP - 1620
AU - Jun JIANG
AU - Di WU
AU - Qizhi TENG
AU - Xiaohai HE
AU - Mingliang GAO
PY - 2015
DO - 10.1587/transinf.2015EDL8011
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2015
AB - Collective motion stems from the coordinated behaviors among individuals of crowds, and has attracted growing interest from the physics and computer vision communities. Collectiveness is a metric of the degree to which the state of crowd motion is ordered or synchronized. In this letter, we present a scheme to measure collectiveness via link prediction. Toward this aim, we propose a similarity index called superposed random walk with restarts (SRWR) and construct a novel collectiveness descriptor using the SRWR index and the Laplacian spectrum of a network. Experiments show that our approach gives promising results in real-world crowd scenes, and performs better than the state-of-the-art methods.
ER -
English
