Estimating Crowd Density from Optical Flow
Carlos Garcia ([email protected]) , Waqas Sultani ([email protected])
Research Experience for Undergraduates 2012, University of Central Florida
Problem
Large uncontrollable crowds have the potential to cause serious
injuries, and even death.
In a disaster, escape paths can be blocked by frightened dense
crowds of people trying to push their way out. This type of
behavior has been shown to cause injuries and death.
During competitions where people must run closely together,
someone can trip and potentially get trampled.
Method 1:
Step 1) Obtain the optical flow of the video, u and v.
Step 2) Compute the magnitude of the velocity from u and v.
Step 3) Locate all of the local maxima.
Step 4) Count the all of the local maxima.
Step 5) Divide all of the resulting numbers by the same threshold value (7 was used for the example shown below).
A way to prevent these disasters from happening is to set up
cameras where dense crowds form and monitor the area. The
system would count the number of people passing through the
vision of the camera and calculate the crowd’s speed, pressure,
and other useful data that can be used to notify a security
personnel that a negative event is about to occur.
Methodology
Method 2:
Step 1) Compute the optical flow of the video.
Step 2) Use the equations below to estimate the acceleration due to pressure, viscosity, social forces, and convection.
Step 3) Use this information to estimate the density (have not completed this yet; still working on it).
www.PosterPresentations.com
Percent Error
Method 1
11.213895 %
Method 2
N/A
Method 3
82.230222 %
Conclusion
Method #1 is an incredibly simple algorithm. 11% is pretty good
considering the algorithm will never catch a person standing still.
These methods will only work when velocity is present.
Method #3 did very poorly on the testing because of the conversions and
parameters needed to compute the density. Further testing is
needed to fine-tune that particular algorithm.
Future Research
Method 3:
Step 1) Compute the optical flow of the video.
Step 2) Compute the magnitude of the velocity.
Step 3) Use linear equation recovered from research paper to estimate the density.
1) Finish working on the fluid dynamics method. There is possibly
a way to relate the different accelerations calculated using a
homogeneous first-order linear partial differential equation.
Although, further research is needed to see if an estimation is
possible from solving it.
2) Continuous particle advection with aging and energy fields.
3) Fluid dynamics open systems.
Acknowledgements
Thank you for your support and helpful advice:
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RESEARCH POSTER PRESENTATION DESIGN © 2012
Method
Method #2 was not tested because it is not completed yet.
Method 1: Number of local maxima of flow field. To explain why
this is useful, an example is given; imagine watching a fish
swimming in water. The water molecules around the fish will be
moving slightly slower than the rest of the fish. The number of
local maxima contained in the flow field comprising the fish’s
movement would be very close to “1.” Now imagine counting
the number of local maxima in the flow field of a school of fish.
This number would be proportional to the number fish in the
school.
Method 3: Linear relationship between density and velocity.
This method follows the simple idea that if a crowd slows down
its density is increasing, while speeding up indicates density is
decreasing. People will move faster if there is space to fill, and
slower if obstacles are in their path.
Dataset: Many videos retrieved from YouTube, REU students,
graduate students, and other various sources from the Internet.
The percent error is based on an average of 25 density computed
frames from one video. Each method was tested on the same 25
frames.
The goal of this research is to find a way to calculate the number
of people in a dense crowd with only its flow field. Below are a
number of methods that were tested to count people in crowds.
Method 2: Fluid dynamics. As crowds get more and more dense,
they begin to look like particles interacting with each other.
Dense crowds show granular particle behavior. Fluid dynamics
could shed some insight on what the density may look like
depending on the interactions between the particles.
Results
Process
Dr. Mubarak Shah
Dr. Niels da Vitoria Lobo
Waqas Sultani
Eraldo Ribeiro