Vision-Based Analysis of Small
Groups in Pedestrian Crowds
Weina Ge, Robert T. Collins,
R. Barry Ruback
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND
MACHINE INTELLIGENCE, VOL. 34, NO. 5, MAY 2012
Goal
Outline
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Introduction
Background and related work
Detecting and tracking individuals
Identifying small groups
Experimental evaluation
Conclusion
Introduction
• There has been increasing interest in using
surveillance trajectory data for human
behavior analysis.
• This paper discover small groups of people
who are together.
Introduction
• A small group behavior suggests new
strategies for police intervention during public
disturbances.
• Police should look at small groups, only a few
of which might merit coercion.
• This paper demonstrates that computer vision
is a capable for supporting sociological
analysis.(crowds data)
Background and related work
• 89 percent of people with at least one other
person 52 percent with 2 , 32 percent with 3,
and that 94 percent left with whom they came
with. [8]
• So divide a crowd of people into small
pedestrain groups is useful for identification
group behavior.
[8] C. McPhail, “Withs across the Life Course of Temporary Sport Gatherings,”
unpublished manuscript, Univ. of Illinois, 2003.
Background and related work
• Collective locomotion behavior is also studied
in the traffic analysis and crowd simulation
community.
• Microscopic level model is suitable for
evacuation planning than macroscopic
model[40].
[40] A. May, Traffic Flow Fundamental. Prentice Hall, 1990.
Detecting and tracking individuals
• For videos captured from high elevation/wide
angle views where people are small, detected
by using (RJMCMC) to find a set of
overlapping rectangles.
Detecting and tracking individuals
• For higher resolution videos, We use the HoG
detector, implemented from the description in
Dalal and Triggs [61].
• The camera is stationary, so background
subtraction is useful for detecting.
[61]N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” Proc.
IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 886-893, 2005
Detecting and tracking individuals
• Sets of tracklets extracted in overlapping
sliding windows of time are combined into
longer trajectories.
• Make a N(trajectories)*M(tracklets) affinity
table.
• best assignment of trajectories is solved by
Hungarian algorithm[64].
[64] H.W. Kuhn, “The Hungarian Method for the Assignment Problem,” Naval Research Logistics
Quarterly, vol. 2, pp. 83-97, 1955.
Detecting and tracking individuals
• Trajectories for which there is no matching
tracklet have their “health” decremented.
Identifying small groups
• Consider the trajectory of a person in the
scene as a set of tuples (s,v,t)
Identifying small groups
• intergroup closeness between two groups of
people by a generalized, symmetric Hausdorff
distance
Identifying small groups
Identifying small groups
• Within each temporal slice, starting from
clusters with a single member, we gradually
group people by agglomerative hierarchical
clustering.
• Each merging step is governed by intergroup
closeness(Hausdorff), and intragroup
tightness.
Experimental evaluation
Experimental evaluation
• The ground truth data , coded by several
coders .
• Coders can replay the sequence if they want
to, the ground truth groups are their
agreements.
• Another set of ground truth are made by
interviewing the people walked in the video.
Experimental evaluation
Kapa test
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agreement=(20+15)/50=0.7
P(A)yes = 0.5
P(B)yes = 0.6
• Random agreement=0.5*0.6+0.5*0.4=0.5
Experimental evaluation
Experimental evaluation
Experimental evaluation
Experimental evaluation
Experimental evaluation
Conclusion
• Results demonstrate that automated tracking
is capable of real crowds faster and with
similar accuracy as human observation.
• Our future work is investigation of small group
configurations across different social events.