Abstract
The goal of this work is to provide a system which can aid in monitoring crowded urban environments, which often contain tight groups of people. In this paper, we consider the problem of counting the number of people in the scene and also tracking them reliably. We propose a novel method for detecting and estimating the count of people in groups, dense or otherwise, as well as tracking them. Using prior knowledge obtained from the scene and accurate camera calibration, the system learns the parameters required for estimation. This information can then be used to estimate the count of people in the scene, in real-time. Groups are tracked in the same manner as individuals, using Kalman filtering techniques. Favorable results are shown for groups of various sizes moving in an unconstrained fashion.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 43-59 |
| Number of pages | 17 |
| Journal | Computer Vision and Image Understanding |
| Volume | 110 |
| Issue number | 1 |
| DOIs | |
| State | Published - Apr 1 2008 |
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Keywords
- Count estimation
- Groups
- Occlusions
- Pedestrian tracking
- Projection
Cite this
Estimating pedestrian counts in groups. / Kilambi, Prahlad; Ribnick, Evan; Joshi, Ajay J.; Masoud, Osama; Papanikolopoulos, Nikolaos P.
In: Computer Vision and Image Understanding, Vol. 110, No. 1, 01.04.2008, p. 43-59.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Estimating pedestrian counts in groups
AU - Kilambi, Prahlad
AU - Ribnick, Evan
AU - Joshi, Ajay J.
AU - Masoud, Osama
AU - Papanikolopoulos, Nikolaos P
PY - 2008/4/1
Y1 - 2008/4/1
N2 - The goal of this work is to provide a system which can aid in monitoring crowded urban environments, which often contain tight groups of people. In this paper, we consider the problem of counting the number of people in the scene and also tracking them reliably. We propose a novel method for detecting and estimating the count of people in groups, dense or otherwise, as well as tracking them. Using prior knowledge obtained from the scene and accurate camera calibration, the system learns the parameters required for estimation. This information can then be used to estimate the count of people in the scene, in real-time. Groups are tracked in the same manner as individuals, using Kalman filtering techniques. Favorable results are shown for groups of various sizes moving in an unconstrained fashion.
AB - The goal of this work is to provide a system which can aid in monitoring crowded urban environments, which often contain tight groups of people. In this paper, we consider the problem of counting the number of people in the scene and also tracking them reliably. We propose a novel method for detecting and estimating the count of people in groups, dense or otherwise, as well as tracking them. Using prior knowledge obtained from the scene and accurate camera calibration, the system learns the parameters required for estimation. This information can then be used to estimate the count of people in the scene, in real-time. Groups are tracked in the same manner as individuals, using Kalman filtering techniques. Favorable results are shown for groups of various sizes moving in an unconstrained fashion.
KW - Count estimation
KW - Groups
KW - Occlusions
KW - Pedestrian tracking
KW - Projection
UR - http://www.scopus.com/inward/record.url?scp=40649113631&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=40649113631&partnerID=8YFLogxK
U2 - 10.1016/j.cviu.2007.02.003
DO - 10.1016/j.cviu.2007.02.003
M3 - Article
VL - 110
SP - 43
EP - 59
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
SN - 1077-3142
IS - 1
ER -