IEICE TRANSACTIONS on Fundamentals
Measuring Crowd Collectiveness via Compressive Sensing
Summary :
Crowd collectiveness, i.e., a quantitative metric for collective motion, has received increasing attention in recent years. Most of existing methods build a collective network by assuming each agent in the crowd interacts with neighbors within fixed radius r region or fixed k nearest neighbors. However, they usually use a universal r or k for different crowded scenes, which may yield inaccurate network topology and lead to lack of adaptivity to varying collective motion scenarios, thereby resulting in poor performance. To overcome these limitations, we propose a compressive sensing (CS) based method for measuring crowd collectiveness. The proposed method uncovers the connections among agents from the motion time series by solving a CS problem, which needs not specify an r or k as a priori. A descriptor based on the average velocity correlations of connected agents is then constructed to compute the collectiveness value. Experimental results demonstrate that the proposed method is effective in measuring crowd collectiveness, and performs on par with or better than the state-of-the-art methods.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E98-A No.11 pp.2263-2266
- Publication Date
- 2015/11/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E98.A.2263
- Type of Manuscript
- Special Section LETTER (Special Section on Smart Multimedia & Communication Systems)
- Category
Authors
Jun JIANG
Sichuan University,Southwest Petroleum University
Xiaohong WU
Sichuan University
Xiaohai HE
Sichuan University
Pradeep KARN
Sichuan University
Keyword
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Jun JIANG, Xiaohong WU, Xiaohai HE, Pradeep KARN, "Measuring Crowd Collectiveness via Compressive Sensing" in IEICE TRANSACTIONS on Fundamentals,
vol. E98-A, no. 11, pp. 2263-2266, November 2015, doi: 10.1587/transfun.E98.A.2263.
Abstract: Crowd collectiveness, i.e., a quantitative metric for collective motion, has received increasing attention in recent years. Most of existing methods build a collective network by assuming each agent in the crowd interacts with neighbors within fixed radius r region or fixed k nearest neighbors. However, they usually use a universal r or k for different crowded scenes, which may yield inaccurate network topology and lead to lack of adaptivity to varying collective motion scenarios, thereby resulting in poor performance. To overcome these limitations, we propose a compressive sensing (CS) based method for measuring crowd collectiveness. The proposed method uncovers the connections among agents from the motion time series by solving a CS problem, which needs not specify an r or k as a priori. A descriptor based on the average velocity correlations of connected agents is then constructed to compute the collectiveness value. Experimental results demonstrate that the proposed method is effective in measuring crowd collectiveness, and performs on par with or better than the state-of-the-art methods.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E98.A.2263/_p
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@ARTICLE{e98-a_11_2263,
author={Jun JIANG, Xiaohong WU, Xiaohai HE, Pradeep KARN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Measuring Crowd Collectiveness via Compressive Sensing},
year={2015},
volume={E98-A},
number={11},
pages={2263-2266},
abstract={Crowd collectiveness, i.e., a quantitative metric for collective motion, has received increasing attention in recent years. Most of existing methods build a collective network by assuming each agent in the crowd interacts with neighbors within fixed radius r region or fixed k nearest neighbors. However, they usually use a universal r or k for different crowded scenes, which may yield inaccurate network topology and lead to lack of adaptivity to varying collective motion scenarios, thereby resulting in poor performance. To overcome these limitations, we propose a compressive sensing (CS) based method for measuring crowd collectiveness. The proposed method uncovers the connections among agents from the motion time series by solving a CS problem, which needs not specify an r or k as a priori. A descriptor based on the average velocity correlations of connected agents is then constructed to compute the collectiveness value. Experimental results demonstrate that the proposed method is effective in measuring crowd collectiveness, and performs on par with or better than the state-of-the-art methods.},
keywords={},
doi={10.1587/transfun.E98.A.2263},
ISSN={1745-1337},
month={November},}
Copy
TY - JOUR
TI - Measuring Crowd Collectiveness via Compressive Sensing
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2263
EP - 2266
AU - Jun JIANG
AU - Xiaohong WU
AU - Xiaohai HE
AU - Pradeep KARN
PY - 2015
DO - 10.1587/transfun.E98.A.2263
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E98-A
IS - 11
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - November 2015
AB - Crowd collectiveness, i.e., a quantitative metric for collective motion, has received increasing attention in recent years. Most of existing methods build a collective network by assuming each agent in the crowd interacts with neighbors within fixed radius r region or fixed k nearest neighbors. However, they usually use a universal r or k for different crowded scenes, which may yield inaccurate network topology and lead to lack of adaptivity to varying collective motion scenarios, thereby resulting in poor performance. To overcome these limitations, we propose a compressive sensing (CS) based method for measuring crowd collectiveness. The proposed method uncovers the connections among agents from the motion time series by solving a CS problem, which needs not specify an r or k as a priori. A descriptor based on the average velocity correlations of connected agents is then constructed to compute the collectiveness value. Experimental results demonstrate that the proposed method is effective in measuring crowd collectiveness, and performs on par with or better than the state-of-the-art methods.
ER -
English
