Everybody needs somebody: Modeling
social and grouping behavior on a linear
programming multiple people tracker
Laura Leal-Taix´e, Gerard Pons-Moll and
Bodo Rosenhahn
ICCV2011
Outline
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Goal
Multiple people tracking
Modeling social behavior
Experimental results
Conclusion
Goal
• People detection is not always correct.
• It is important to merge the detection results
into right trajectoies.
Multiple people tracking
• divided in two steps
– object detection
– data associationform complete trajectories
• Build a graph with the nodes pedestrian
detections
• The matching problem is equivalent to
minimum-cost network flow problem
Multiple people tracking
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• Find the
detection.
• 4
,trajectory of k
that best explains the
• P(oi|T) is the likelihood.
Multiple people tracking
• trajectory Tk have following dependencies
– Constant velocity assumption
find oi depends on oi-1,oi-2
– Grouping behavior
– Avoidance term
Multiple people tracking
• Represent by Markov chain:
Multiple people tracking
Multiple people tracking
• Combine (1),(2),(3)
Multiple people tracking
• Three kinds of edges:
– Link edges
– Detection edges
– Entrance and exit edges
Multiple people tracking
• Link edges
• The edges (ei, bj) connect the end nodes ei
with the beginning nodes bj in following
frames,with cost Ci,j and flag fi,j
• Flag =1 if oi and oj belong to Tk,and ∆f≤Fmax
• 111
Multiple people tracking
• Detection edges
• The edges (bi, ei) connect the beginning node
bi and end node ei, with cost Ci and flag fi
Modeling social behavior
• If a pedestrian doesn’t meet any obstacles, he
will naturally follow a straight line.
• But the pedestrian will have some social
behavior.
• Add Social Force Model (SFM)and Group
behavior(GR) into the problem.
Modeling social behavior
• Social forces have three main terms:
– The desire to maintain certain speed
– The desire to keep away from others
– The desire to reach a destination
• We focus on first two!
Modeling social behavior
• Constant velocity assumpion
– When a person walk at a speed V at time t
– We assume he will have speed V at time t+∆t
Modeling social behavior
• Avoidance term
Modeling social behavior
• From the training sequence in [22] , we learn
the probabilty of Pg and Pi
[22] S. Pellegrini, A. Ess, K. Schindler, and L. van Gool. You’ll never walk alone:
modeling social behavior for multi-target tracking. ICCV, 2009. 1, 2, 5, 7
Experimental results
Blue=>DIST
Greed=>with SDM
Red=>SFM+GR
Experimental results
Experimental results
• To show the importance of social behavior and
the robustness of our algorithm at low frame
rates, we track at 2.5fps (taking one every
tenth frame).
Experimental results
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DA (detection accuracy)
TA (tracking accuracy)
DP (detection precision)
TP (tracking precision)
Experimental results
[28]use network flow
[22]use social behavior
[27] use social and grouping
Experimental results
Conclusion
• It is important to have social and group
relation on tracking.
• This paper outperform on low fps than others
and have high accuracies on miss
detections,false alarms and noise.