Recursive Bayesian estimators for people counting using video sensors
- Publication Type:
- Thesis
- Issue Date:
- 2007
Closed Access
Filename | Description | Size | |||
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01Front.pdf | contents and abstract | 589.11 kB | |||
02Whole.pdf | thesis | 33.22 MB |
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NO FULL TEXT AVAILABLE. Access is restricted indefinitely. ----- Real time pedestrian flow information and the count of people in determined
areas are essential for a multitude of facility management and monitoring functions,
including the optimal utilization of floor space. The range of applications
is wide and the focus here is on the development of statistical methods for people
volume estimation in buildings. To count people, visual information from
surveillance cameras is used. This is the first of a series of reports, where the
problem is divided in estimation classes of increasing difficulty. There is no
widespread use of intelligent systems for surveillance. In addition, most of the
existing systems are limited in scope and provide answers only for areas covered
by cameras. We propose to develop a generic approach for estimating crowds
in large locations, based on partial information. We propose to do so using
probabilistic modeling. We first model the number of people in camera covered
environments. We then gradually enlarge the scope of the problem to provide
an answer for the estimation of the pedestrian flow in locations such as large
buildings. In our approach, we propose to develop probabilistic models for the
count of people in the views of cameras at time t, N(t), and estimate the number
of people in areas not covered by cameras.
In the initial stage, probability models are derived for the basic counting scenario,
using data gathered from a single camera. The methodology is extended
to more complex problems, making use of several cameras covering the same
field of view. The consideration of all possible classes of estimation problems
leads to solutions that culminate in the assessment of the total number of people
in a building. We present a systematic approach to extracting vital statistics like
arrival/departure rates and stay times across the facility from visual sensors.
We use an object tracker to extract people related data from the video sensors.
The number of people in the covered area is determined. The inter-arrival times
have Exponential Distribution with mean(λ), this results in the arrival stochastic
process being a Poisson process within a homogeneous period. The arrival
process throughout the day is the non-homogeneous Poisson process (NHPP).
We use a Bayesian approach with a conjugate prior distribution Gamma for λ
parameter that leads to a posterior Gamma distribution. Statistics for the stay
times are deducted from the stochastic process of the number of incoming people.
A Gamma probability model is assumed for the stay times with unknown
parameters alpha and beta. The Bayesian methodology is applied to model
these parameters probabilistically, using the statistical data.
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